ILLINOIS

INDIANA

LAKE

MICHIGAN

Prepared in cooperation with the U.S. Army Corps of Engineers, Chicago District

Analysis of Regional Rainfall-Runoff Parameters for 

the Lake Michigan Diversion Hydrological Modeling

Scientific Investigations Report 2015–5053

U.S. Department of the Interior

U.S. Geological Survey
Cover: The image, also shown in figure 1, shows the National Land Cover Database, 2006. (http://www.mrlc.gov/nlcd2006.php)
Analysis of Regional Rainfall-Runoff 

Parameters for the Lake Michigan 

Diversion Hydrological Modeling

By David T. Soong and Thomas M. Over

Prepared in cooperation with the U.S. Army Corps of Engineers, Chicago District

Scientific Investigations Report 2015–5053

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

resources, natural hazards, and the environment—visit http://www.usgs.gov or call 1–888–ASK–USGS.

For an overview of USGS information products, including maps, imagery, and publications, 

visit http://www.usgs.gov/pubprod/.

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

U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Soong, D.T., and Over, T.M., 2015, Analysis of regional rainfall-runoff parameters for the Lake Michigan Diversion 

hydrological modeling: U.S. Geological Survey Scientific Investigations Report 2015–5053, 55 p., http://dx.doi.

org/10.3133/sir20155053.

ISSN 2328-0328 (online)
iii

Contents

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

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

Hydrological Simulation Program-FORTRAN Model and Parameter Sets...........................................3

Historic Runoff Parameter Sets..........................................................................................................7

Reevaluation of Parameter Sets ........................................................................................................8

Purpose and Scope ..............................................................................................................................8

Preparation of Models and Input Data.....................................................................................................10

Determination of Land Cover............................................................................................................11

Meteorological Data...........................................................................................................................13

Streamflow Data .................................................................................................................................13

Effluent Discharges and Water Withdrawals ................................................................................18

Soil Types .............................................................................................................................................19

Accuracy of Regional Hydrological Simulation Program-FORTRAN Parameter Sets Used in 

Lake Michigan Diversion Accounting.........................................................................................19

Simulated to Recorded Ratios ..........................................................................................................21

Double-Mass Curve Analysis............................................................................................................24

Flow Duration Curves.........................................................................................................................24

Quantitative Statistics........................................................................................................................31

Case for Recalibration .......................................................................................................................32

Recalibration of PERLND Parameters......................................................................................................33

Manual Calibration .............................................................................................................................33

Automatic Calibration ........................................................................................................................35

Design of PEST calibration.......................................................................................................35

Objective Function.....................................................................................................................36

Parameter Ratios .......................................................................................................................36

Weight for Observation Groups...............................................................................................37

Initial Calibration........................................................................................................................37

Parameter Sensitivity ...............................................................................................................37

Refinement Calibration .............................................................................................................37

Analysis of Calibrated Parameter Sets ...........................................................................................39

Summary........................................................................................................................................................53

References Cited..........................................................................................................................................55

Figures

1. Map showing study area, including locations of nine watersheds selected for testing 

model parameters, the diverted Lake Michigan watershed, meteorological and

precipitation stations, and the land-use/land-cover classes based on 2006 

National Land Cover Database...................................................................................................2

2. Diagram showing Hydrological Simulation Program-FORTRAN (HSPF) flowchart for 

hydrologic process for pervious land segment ......................................................................4
iv

3. Graph showing normalized monthly streamflow volume and total monthly precipitation 

for the nine study watersheds for the period from October 1, 1995, to September 30,

2011. The red line is the average of normalized monthly streamflow volume of the

nine watersheds over the study period ..................................................................................15

4. Graph showing normalized annual streamflow volume and total monthly precipitation 

for the nine study watersheds for the period from October 1, 1995, to September 30, 

2011. The red line is the average of normalized annual streamflow volume of the 

nine watersheds over the study period ..................................................................................16

5. Graph showing flow duration curves based on mean-daily discharge between 

water years 1996 and 2011 for the nine study watersheds..................................................17

6. Map showing SCS hydrologic soil groups for Cook County and the nine study 

watersheds ..................................................................................................................................20

7. Graphs showing double-mass curves of observed streamflow and simulated runoff 

volume for the nine study watersheds using the North Branch parameter set for 

water years 1996 to 2011 ...........................................................................................................25

8. Graphs showing double-mass curves of observed streamflow and simulated runoff 

volume for the nine study watersheds using the 1999 parameter set for water years 

1996 to 2011..................................................................................................................................26

9. Graphs showing double-mass curves of observed streamflow and simulated runoff 

volume for the nine study watersheds using the 2008 parameter set for water years 

1996 to 2011..................................................................................................................................27

10. Graphs showing flow-duration curves of observed streamflow and simulated runoff 

volume for the nine study watersheds using the North Branch parameter set for 

water years 1996 to 2011 ...........................................................................................................28

11. Graphs showing flow-duration curves of observed streamflow and simulated runoff 

volume for the nine study watersheds using the 1999 parameter set for the water 

years 1996 to 2011.......................................................................................................................29

12. Graphs showing flow-duration curves of observed streamflow and simulated runoff 

volume for the nine study watersheds using the 2008 parameter set for water 

years 1996 to 2011.......................................................................................................................30

13. Graphs showing double-mass curves of observed streamflow and simulated runoff 

volumes for the nine study watersheds using calibrated parameter set obtained 

from the HSPEXP Tinley Creek watershed model for water years 1996 to 2011 ..............45

14. Graphs showing double-mass curves of observed streamflow and simulated runoff 

volumes for the nine study watersheds using calibrated parameter set obtained from 

the HSPEXP Long Run watershed model for water years 1996 to 2011.............................46

15. Graphs showing double-mass curves of observed streamflow and simulated runoff 

volumes for the nine study watersheds using calibrated parameter set obtained 

from the PEST Tinley Creek watershed model for water years 1996 to 2011....................47

16. Graphs showing double-mass curves of observed streamflow and simulated runoff 

volumes for the nine study watersheds using calibrated parameter set obtained 

from the PEST Long Run watershed model for water years 1996 to 2011.........................48

17. Graphs showing double-mass curves of observed streamflow and simulated runoff 

volumes for the nine study watersheds using calibrated parameter set obtained 

from the PEST two-watershed model for water years 1996 to 2011 ..................................49
v

Tables

1. Rainfall-runoff parameters used in the Hydrological Simulation Program-FORTRAN

program and their effects on various components of the water budget ............................5

2. Historic rainfall-runoff parameters and values for grassland in the Hydrological

Simulation Program-FORTRAN program used in the diversion accounting system.

Data adapted from U.S. Army Corps of Engineers, Chicago District (2004), and

expanded to include Consoer Townsend EnviroDyne Engineers parameter set...............8

3. Historic rainfall-runoff parameter values for forestland in the Hydrological

Simulation Program-FORTRAN program used in the diversion accounting system.........9

4. Historical rainfall-runoff parameter values for impervious land in the Hydrological

Simulation Program-FORTRAN program used in the diversion accounting system.

Data adapted from U.S. Army Corps of Engineers, Chicago District (2004)........................9

5. Background information for the selected watersheds ........................................................10

6. Percentages of the Lake Michigan Diversion Accounting land-cover categories

including forestland, grassland, and hydraulically connected impervious lands for

the study watersheds and their counterpart land-cover classes according to the

2006 National Land Cover Database........................................................................................11

7. Matrix for conversion of National Land Cover Database land-cover classes to Lake

Michigan Diversion Accounting land-cover types ...............................................................12

8. Comparison of percentages of grassland, forestland, and hydraulically connected

impervious land determined by 2005 aerial photographs and conversion of 2006

National Land Cover Database land-use categories............................................................12

9. Modeled land-cover classes determined from National Land Cover Database 

1992, 2001, and 2006 datasets for nine selected watersheds..............................................14

10. The maximum discharge, mean discharge, minimum discharge, and standard

deviation determined from the mean daily discharge record for the nine study

watersheds, October 1, 1995, to September 30, 2011 ...........................................................15

11. Effluent discharge data in the study area during 1998 to 2012 ...........................................18

12. Recommended statistical tests and ratings for evaluation of model-simulation

performance ................................................................................................................................21

13. Ratio of simulated runoff to recorded streamflow volume for annual and total flow 

volumes between water years 1996 and 2011 for nine study watersheds with the 

six parameter sets ......................................................................................................................22

14. Statistics of the mean monthly runoff volume at nine study watersheds simulated

by the existing parameter sets for the period from October 1, 1995, to 

September 30, 2011.....................................................................................................................31

15. Parameter values and calibration results for the manual calibration of Tinley Creek

and Long Run watershed models.............................................................................................34

16. Observation group to parameter sensitivity relations obtained from the initial 

Tier-3 analysis in PEST Tinley Creek and PEST Long Run watershed models..................38

17. Grassland and forestland parameters obtained from HSPEXP and PEST 

calibrations ..................................................................................................................................40

18. Comparison of basic flow statistics and fraction of base flow volume derived from

measured daily flows and simulated daily runoff time series derived with the

HSPEXP and PEST calibration parameters for the calibration period between

October 1, 2000, and September 30, 2009 ...............................................................................41

19. Quantitative statistics of PBIAS, NSE, and RSR for the calibration and verification

periods for parameters obtained from HSPEXP and PEST calibrations............................42
vi

20. Ratio of simulated annual runoff volume to recorded annual streamflow volume

determined from HSPEXP and PEST calibrations at the nine study watersheds 

for water years 1996 to 2011 .....................................................................................................44

21. Quantitative statistics of PBIAS, NSE, RSR for the study periods, water years

1996 to 2011, for the North Branch parameter set and PEST two-watershed 

parameter set..............................................................................................................................50

22. Calibrated parameter values of grassland and forestland for the nine study 

watersheds with description of the sources for estimating the parameter values ........51

Conversion Factors

Inch/Pound to International System of Units

Multiply By To obtain

Length

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

yard (yd) 0.9144 meter (m)

Area

acre 4,047 square meter (m2)

square mile (mi2) 2.590 square kilometer (km2) 

Volume

cubic foot (ft3) 0.02832 cubic meter (m3) 

acre-foot (acre-ft) 1,233 cubic meter (m3)

Flow rate

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

cubic foot per second (ft3/s) 1.983 acre-feet/day

cubic foot per second per 

square mile [(ft3/s)/mi2]

 0.01093 cubic meter per second per 

square kilometer [(m3/s)/km2]

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

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

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

A water year is the 12-month period from October 1 through September 30 and is designated by 

the calendar year in which it ends and includes 9 of 12 months. For example, WY 2014 is from 

October 1, 2013, to September 30, 2014.
vii

Acknowledgments

Tzuoh-Ying Su, U.S. Army Corps of Engineers, Chicago District, provided valuable coordination 

and technical support throughout the project. Nancy Westcott, Atmospheric Science Section 

of the Illinois State Water Survey, Prairie Research Institute, University of Illinois at UrbanaChampaign, provided information about the 25-raingage network. Tracey Larmon of Flagg Creek 

Water Reclamation District provided effluent discharge data for the Hinsdale sewage treatment 

plant. Carey A. Johnston, U.S. Environmental Protection Agency Office of Compliance and 

staff at the Illinois Environmental Protection Agency assisted in the retrieval of effluent discharge data from facilities in the study area. John Doherty, developer of PEST program, kindly 

responded to questions regarding the approaches used in the study. 

Tamara I. Ivanhenko of U.S. Geological Survey (USGS) provided initial effluent data retrieved 

from the Permit Compliance System (PCS) of the U.S. Environmental Protection Agency (EPA) in 

the study region. USGS colleagues also assisted in various components of the study: Andrew 

Reinhard and Rachel Egly assisted in data processing and analysis, model preparation, and 

figure preparations; Katherine R. Merriman-Hoehne helped update the effluent data from the 

PCS of the EPA and assisted in manual model calibration; and Jennifer B. Sharpe prepared the 

preliminary illustrations. Suggestions provided by USGS colleagues Darwin J. Ockerman and 

James J. Duncker greatly improved the report.

Introduction 1

Analysis of Regional Rainfall-Runoff Parameters for the 

Lake Michigan Diversion Hydrological Modeling

By David T. Soong and Thomas M. Over

Abstract

The Lake Michigan Diversion Accounting (LMDA) 

system has been developed by the U.S. Army Corps of 

Engineers, Chicago District (USACE-Chicago) and the State 

of Illinois as a part of the interstate Great Lakes water regulatory program. The diverted Lake Michigan watershed is a 

673-square-mile watershed that is comprised of the Chicago 

River and Calumet River watersheds. They originally drained 

into Lake Michigan, but now flow to the Mississippi River 

watershed via three canals constructed in the Chicago area in 

the early twentieth century. Approximately 393 square miles 

of the diverted watershed is ungaged, and the runoff from the 

ungaged portion of the diverted watershed has been estimated 

by the USACE-Chicago using the Hydrological Simulation Program-FORTRAN (HSPF) program. The accuracy of 

simulated runoff depends on the accuracy of the parameter set 

used in the HSPF program. Nine parameter sets comprised of 

the North Branch, Little Calumet, Des Plaines, Hickory Creek, 

CSSC, NIPC, 1999, CTE, and 2008 have been developed 

at different time periods and used by the USACE-Chicago. 

In this study, the U.S. Geological Survey and the USACEChicago collaboratively analyzed the parameter sets using 

nine gaged watersheds in or adjacent to the diverted watershed 

to assess the predictive accuracies of selected parameter sets. 

Six of the parameter sets, comprising North Branch, Hickory 

Creek, NIPC, 1999, CTE, and 2008, were applied to the nine 

gaged watersheds for evaluating their simulation accuracy 

from water years 1996 to 2011. The nine gaged watersheds 

were modeled by using the three LMDA land-cover types 

(grass, forest, and hydraulically connected imperviousness) 

based on the 2006 National Land Cover Database, and the 

latest meteorological and precipitation data consistent with the 

current (2014) LMDA modeling framework.

Results indicate that the North Branch and Hickory Creek 

parameter sets, which belong to the original calibration group, 

attained an overall “satisfactory” rating on monthly runoff 

volumes based on the three performance statistics selected, but 

the annual and over-the-period runoff volumes were generally underestimated. Parameter sets CTE and 2008 attained a 

similar satisfactory rating on monthly runoff volumes but the 

annual and over-the-period runoff volumes were overestimated 

in general. Although the percent bias was improved, the 

CTE and 2008 parameter sets also had increased residuals in 

monthly runoff volumes and decreased quality of the model fit 

to the measured streamflows relative to the North Branch and 

Hickory Creek parameter sets. The NIPC and 1999 parameter 

sets, on the other hand, had larger percent bias and residuals in 

monthly runoff volumes, and underestimated the annual and 

over-the-period runoff volumes.

Recalibration of the HSPF parameters to the updated 

inputs and land covers was completed on two representative 

watershed models selected from the nine by using a manual 

method (HSPEXP) and an automatic method (PEST). The 

objective of the recalibration was to develop a regional parameter set that improves the accuracy in runoff volume prediction for the nine study watersheds. Knowledge about flow and 

watershed characteristics plays a vital role for validating the 

calibration in both manual and automatic methods. The best 

performing parameter set was determined by the automatic 

calibration method on a two-watershed model. Applying this 

newly determined parameter set to the nine watersheds for 

runoff volume simulation resulted in “very good” ratings in 

five watersheds, an improvement as compared to “very good” 

ratings achieved for three watersheds by the North Branch 

parameter set.

Introduction 

Illinois’ Lake Michigan Diversion is made up of three 

components, domestic water supply, direct diversion, and 

storm water runoff (Illinois Department of Natural Resources, 

Office of Water Resources, http://www.dnr.illinois.gov/

WaterResources/Pages/LakeMichiganWaterAllocation.aspx, 

accessed March 23, 2015). The diverted Lake Michigan watershed (figure 1, hereafter diverted watershed) is a 673-squaremile watershed that is comprised of the Chicago River and 

Calumet River watersheds. Flows from the two rivers which 

originally drained into Lake Michigan now are diverted to the 

Mississippi River watershed via three canals constructed in 

the Chicago area in the early twentieth century. In response 

to a 1967 Supreme Court Decree amended in 1980, a Lake 
2 Lake Michigan Diversion Accounting Land Cover Change Estimation

col13-EMNE00-1359 Soong Figure 1

LAKE

WILL LAKE

DUPAGE

COOK

ILLINOIS

INDIANA

Lake Michigan

01

03

05

08

11

15

16

20

Chicago Botanical Gardens

Chicago

(Romeoville

Lewis

University Airport)

Joliet,

Brandon

Rd Dam

Monee Reservoir

Little Red School House

Flag Creek

Hickory

Creek

Long

Run

Skokie

River

Crown Point 1 N

02

04

06 07

09 10

12 13

17 18 19

21 22

23

24 25

Crete

Joliet

Chicago Midway Airport

Park Forest

Gary

Creek

Hart Ditch

at Dyer

Midlothian

Creek

Hart Ditch

at Munster

Tinley

Creek

Butterfield 

14

05536000

05536290

National Land Cover Database (NLCD) 

2006 from Multi-Resolution Land 

Characteristics Consortium (MRLC)

0 5 10 15 MILES

0 5 10 15 KILOMETERS

Base from U.S. Geological Survey,

1:100,000-scale digital data

Albers Equal-Area Conic projection

Standard parallels 45° and 33°, 

central meridian -89°

88°

42°20’

42°

41°40’

41°20’

87°40’ 87°20’

EXPLANATION

Study watershed boundary

Diverted Lake Michigan 

watershed

Land-cover class 

(National Land Cover 

Dataset, 2006)

Open water

Developed, open space

Developed, low intensity

Developed, medium intensity

Developed, high intensity

Barren land

Deciduous forest

Evergreen forest

Mixed forest

Shrub/scrub

Grassland/herbaceous

Pasture/hay

Cultivated crops

Woody wetlands

Emergent herbaceous 

wetland

National Climate Data Center 

(NCDC) precipitation gage

and name

Raingage and identifier

Meteorological station

U.S. Geological Survey 

streamgage and identifier

Argonne National

Laboratory

14

Gary

05536290

ILLINOIS INDIANA

Map

area

Figure 1. Study area, including locations of nine watersheds selected for testing model parameters, the diverted Lake Michigan 

watershed, meteorological and precipitation stations, and the land-use/land-cover classes based on 2006 National Land Cover 

Database (Fry and others, 2011).
Hydrological Simulation Program-FORTRAN Model and Parameter Sets 3

Michigan Diversion Accounting (LMDA) system has been 

developed by the U.S. Army Corps of Engineers, Chicago 

District (USACE-Chicago) and the State of Illinois as a part of 

the interstate Great Lakes water regulatory program. The 1986 

Water Resources Development Act (WRDA) stipulates that 

the USACE is responsible for monitoring the flow measurement and computation of annual diversion accounting for the 

State of Illinois’ Lake Michigan diversion. (T.Y. Su, written 

commun., March 24, 2015). In the current LMDA system, the 

total runoff volume from the diverted watershed is comprised 

of the streamflow portion of the discharges (effluent removed) 

measured at two U.S. Geological Survey (USGS) streamflow 

gages located at the mid portion of the two rivers, the North 

Branch Chicago River at Niles (05536000, 92 square miles) 

and Little Calumet River at South Holland (05536290, 188 

square miles) plus the runoff estimated for the ungaged portion 

of the diverted watershed (T.Y. Su, written commun., March 

31, 2015). Runoff from the ungaged portion of the diverted 

watershed (393 square miles) is estimated by the Hydrological 

Simulation Program-FORTRAN (HSPF; Bicknell and others, 

2000). Depending on the amount of precipitation, the annual 

total runoff volume from the diverted watershed amounts 

to approximately 30 percent of the annual total diversion 

volumes.

Runoff has been simulated by using the same rainfallrunoff parameter set in all HSPF models covering the ungaged 

portion of the diverted watershed (that is, a regional parameter 

set approach), together with respective measured meteorological data including measured precipitation, air temperature, 

dew point temperature, wind speed, cloud cover, solar radiation, and computed potential evapotranspiration. The detailed 

descriptions of the background, authorities, and accounting 

procedures of the LMDA system are presented in the diversion accounting report for water year (WY, a water year is 

the 12-month period from October 1 through September 30) 

2011 by the USACE-Chicago District (U.S. Army Corps 

of Engineers, 2014) and the review report by the Seventh 

LMDA Technical Committee (U.S. Army Corps of Engineers, 

2014). The USGS, in cooperation with the USACE-Chicago 

conducted this collaborative study to assess the accuracy of 

regional rainfall-runoff parameter sets in simulated runoff volumes from gaged watersheds in and adjacent to the diverted 

Lake Michigan watershed. 

Hydrological Simulation ProgramFORTRAN Model and Parameter Sets

The HSPF model (Bicknell and others, 2000) is a continuous, semi-lumped parameter model (Singh, 1995). The 

HSPF model can quantify runoff and water-quality constituent 

and the effects of various land uses and stormwater-management practices on runoff, and address water-quality impairments associated with combined point and nonpoint sources 

through continuous simulation of physically-based processes 

in conceptual pathways of the hydrological cycle in a watershed. The HSPF is public-domain software supported by the 

U.S. Environmental Protection Agency (EPA) and USGS. The 

HSPF rainfall-runoff processes and associated parameters are 

briefly introduced in figure 2 and table 1. A schematic showing the conceptual pathway of HSPF for the rainfall and water 

mass moving through the pervious land segment (PERLND) 

is shown in figure 2. Detailed explanations about the modeling philosophy and parameter descriptions and their functions 

are present in the HSPF user’s manual (Bicknell and others, 

2000). The meteorological and precipitation time series are 

inputs. Precipitation was subject to interception, transpiration, 

and surface detention; some of the remaining water becomes 

direct runoff on land surface and the rest infiltrates into the 

ground, which then moves through zones including interflow 

storage, upper zone storage, lower zone storage, and active 

groundwater storage. Water in the various storage zones is 

subject to retention and evapotranspiration losses; eventually, 

some of the infiltrated water might flow to streams as base 

flow (groundwater outflows) or could be lost to deep groundwater aquifers (inactive groundwater). Base flow remains 

active before, during, and after the storm events, but direct 

surface runoff and interflow occur shortly after a storm (the 

sum is referred to as “quick flow”). The sum of base flow and 

quick flow is the simulated total runoff which is the modeled 

output for comparison with the measured streamflow. For 

an impervious land segment (IMPLND), the infiltration is 

assumed to be zero and there is no groundwater component. 

On an annual basis, a conceptual water balance model is 

expressed by Donigian (2002) as follows. 

precipitation–actual evapotranspiration–deep percolation–

changes in soil moisture storage=total runoff (1)

Some items in the water balance model are not measurable 

and have to be approximated by modeling, and various flow 

components are needed to describe the physical processes 

involved.

The HSPF software uses an array of empirical equations 

to compute the flux and changes in storage in a continuous 

manner. By adjusting key parameters used in these empirical equations to match the measured streamflow of a real 

watershed, an HSPF watershed model is calibrated and could 

be used to simulate outflows in other input or management 

scenarios. Table 1 is a list of parameters, their descriptions 

and effects on flow components included in the PERLND 

and IMPLND segments of the HSPF model. The list is summarized from the HSPF user’s manual (Bicknell and others, 

2000), BASINS Technical Note No. 6 (U.S. Environmental 

Protection Agency, 2000), and literature such as Duncker and 

Melching (1998) and Price (1996). 

A HSPF watershed model is represented with designated 

land covers as separate PERLNDs and IMPLNDs, each with 

its own hydrological parameter values to simulate the runoffs. 

In the LMDA system, three types of land cover are prescribed: 
4 Lake Michigan Diversion Accounting Land Cover Change Estimation

col13-EMNE00-1359 Soong Figure 2

To stream

Actual ET

Potential ET,

precipitation,

temperature,

radiation,

wind, and 

dewpoint

Snowmelt

Interception

storage

Overland flow

Interflow Upper zone

storage

Groundwater

storage

Lower zone

storage

Deep or inactive

groundwater

ET

ET

ET

ET

ET

CEPSC

LZSN

LZETP

AGWETP

DEEPFR

INFILT

UZSN

INTFW

LSUR

SLSUR

NSUR

IRC

AGWRC

BASETP

Direct

infiltration

Delayed

infiltration

2

3

5

4

1

EXPLANATION

ET 1

CEPSC

INFILT

LSUR

NSUR

SLSUR

INTFW

UZSN

IRC

LZSN

LZETP

DEEPFR

Interception storage

Infiltration

Length of overland flow plane

Manning's n for the overland flow plane

Slope of overland flow plane

Interflow

Upper zone storage

Interflow recession constant

Lower zone nominal storage

Lower zone evapotranspiration

Fraction of groundwater inflow that 

goes to inactive groundwater

Base-flow evapotranspiration

Active groundwater evapotranspiration

Active groundwater recession constant

Symbols Parameter

BASETP

AGWETP

AGWRC

Output

Storage

Model decision point

Process

Input

Number denotes the order water is 

taken to meet ET demand

Flow direction

Water lost to evapotranspiration (ET)

Figure 2. Hydrological Simulation Program-FORTRAN (HSPF) flowchart for hydrologic process for pervious land segment 

(adapted from Bicknell and others, 2000).
Hydrological Simulation Program-FORTRAN Model and Parameter Sets 5

Table 1. Rainfall-runoff parameters used in the Hydrological Simulation Program-FORTRAN program and their effects on various 

Parameter Description Unit Effects

FOREST Fraction of land covered in (for example, conifer) forest 

that can transpire when there is snow pack.

none Snowmelt amount in the water balance.

LZSN Lower zone nominal soils moisture storage. inches Affects annual and event runoff volumes through ET, 

base flows, and losses to inactive groundwater determined in lower zone.

INFILT Index of infiltration capacity. in/hr Affects water balance, high/low flow distributions, and 

stormflows through altering the relation between 

groundwater and direct runoff. 

LSUR Length of overland flow plane. feet Storm peaks, no effect on annual runoff volumes.

SLSUR Average slope of assumed overland flow plane. ft/ft Storm peaks, no effect on annual runoff volumes.

KVARY A constant describes the nonlinear groundwater recession.1/in Base flow, seasonal variations, but has almost no effect 

on annual or event runoff volume.

AGWRC A constant describes the base groundwater recession 

rate.

none Base flow, and therefore event runoff, in the context of 

amount assigned to infiltrated and active groundwater.

PETMAX Temperature below which ET is reduced to 50 percent 

of that in the input time series.

°F ET and all related processes; used when SNOW is being 

simulated. 

PETMIN Temperature below which ET is zero. °F Like PETMAX, it affects ET and all related processes; 

used when SNOW is being simulated.

INFEXP Exponent in infiltration equation that determines how 

much a deviation from nominal lower zone storage 

affects the infiltration rate.

none Those affected by INFILT.

INFILD Ratio of maximum/mean soil infiltration capacities. none This ratio has always been set to 2.

DEEPFR Fraction of infiltrating water lost to inactive groundwater (deep percolation).none Primarily the portion of base flow between storms but 

also event runoff volumes as groundwater discharge 

continues.

BASETP ET (specified as fraction of PET) lost to riparian vegetation as active groundwater enters streambed.none Base flow volume (as ET is exerted from the outflow 

portion of active groundwater); annual runoff volume 

in the context of base flow generation.

AGWETP Fraction of PERLND subjects to direct evaporation 

from groundwater storage.

none Base flow volume (as ET is exerted from the storage 

portion of active groundwater); annual runoff volume 

in the context of base flow generation.

CEPSC Interception storage capacity by vegetation. inches Event and annual runoff volume, in particular smaller 

storm events that are sensitive to the interception.

UZSN Nominal upper zone soil moisture storage. inches Direct surface runoff and interflow enter the upper zone 

and the water fluxes out of upper zone. UZSN can 

vary seasonally.

NSUR Manning’s roughness for overland flows. none Storm peaks and event volumes.

INTFW Determines interflow. none The amount of interflows.

IRC Interflow recession parameter. none The rate of interflow becomes surface runoff; hence 

event runoff volume and timing as well as hydrograph 

shapes. 

LZETP Parameter determines lower zone ET. none Annual runoff and some storm events through seasonal 

dormancy of vegetation that draws water from lower 

zone.

RETSC Retention storage capacity. inches Event and annual runoff volumes, in particular smaller 

storm events. 

components of the water budget.

[ET, evapotranspiration; in/hr, inches per hour; ft/ft, foot per foot; 1/in, inverse of one inch; °F, degrees Fahrenheit]
6 Lake Michigan Diversion Accounting Land Cover Change Estimation

grassland, forestland, and hydraulically connected impervious 

land. Grassland and forestland represent the PERLND segments where water movement is simulated by using SNOW 

and PWATER modules. The hydraulically connected impervious land represents the IMPLND segment and water movement is simulated by using SNOW and IWATER modules. 

The simulated total runoff from a watershed model is the 

sum of simulated runoff from each type of land covers used 

in describing the watershed. Simulated runoff volume from 

each type of land cover depends on the meteorological inputs 

and its parameter values and is independent of the simulations by other types of land covers. In HSPF, spatial patterns 

of the PERLND and IMPLND segments are not considered. 

Simulated runoff from all PERLND and IMPLND segments 

appears at the outlet of the watershed. If a watershed model 

is segmented further into subbasins to meet the study needs, 

simulated runoff from each subbasin can be routed with 

hydrologic routing if a reach module (RCHRES) is included. 

Linking RCHRES in a network representing the watershed 

system enables the routed runoff to be specified at the outlet 

of the watershed or other designated locations in the watershed; however, if RCHRES is not considered (which is true in 

LMDA HSPF modeling), then runoffs simulated by PERLND 

and IMPLND segments in each subbasin outflow to the designated outlets instantaneously. 

The quality of the meteorological and precipitation data, 

the representativeness of the land covers (types and acreages), 

the physical processes that are captured by the model, and the 

parameterization are factors that can affect the accuracy of 

runoff simulations. The regional HSPF parameter approach 

means that all watersheds modeled with HSPF in the LMDA 

system are described with respective compositions of grassland, forestland, and hydraulically connected impervious 

land but the parameter values representing the grassland, 

forestland, and hydraulically connected impervious land. Only 

meteorological and precipitation inputs are varied for each 

watershed according to location. The original regional parameter set was determined from calibrating 14 watersheds in and 

near the LMDA area in the 1960s and 1970s (Hydrocomp, 

1977a, b, c, d and 1979; USACE-Chicago, 2004). There have 

been several updates to parameter values and some changes 

may have resulted from the quality and types of data used 

as well as the watersheds selected in the calibration process 

(USACE-Chicago, 2009). These updates include a 1993 

assessment of the runoff parameter values after the precipitation assignments from 25-raingage network replaced the 

original13-raingage network (Peppler, 1991). Also, land cover 

was redelineated from aerial photographs (RUST Environment 

and Infrastructure, 1993a). Since WY 1996, meteorological 

inputs have been modified by (1) using direct solar radiation 

measured at Argonne National Laboratory, (2) incorporating 

the National Weather Service (NWS) 3-hour meteorological 

data recorded at O’Hare Airport for snowmelt computation, 

and (3) switching air temperature data for subareas that previously referenced to the University of Chicago data to either 

the O’Hare Airport, Midway Airport, or Park Forest temperature gage, depending on proximity (USACE-Chicago, 2009). 

The parameter set used between WYs 1997 and 2000 was 

evolved from the calibrated “original” HSPF model, which 

probably resulted from a hydrologic recalibration conducted 

by Hey and others (1980). The Fifth Technical Review Committee (USACE-Chicago, 2004) concluded that this parameter 

set provided acceptable results at Water Reclamation Plants 

(WRPs); however, some parameter values were outside the 

range of previously calibrated values, and evaluating the 

accuracy of the rainfall-runoff simulations by comparing flows 

at the WRPs was not sufficient. The parameter set that has 

been in use since 2000 was developed by Consoer Townsend 

Envirodyne Engineers (CTE), now AECOM, based on a water 

budget analysis at WRPs and the Chicago and Sanitary Ship 

Canal (CSSC) waterway system (T.Y. Su, USACE-Chicago, 

written commun., April 6, 2006). For identification purposes, 

the parameter set determined by Hey and others (1980) is 

hereafter referred to as “1999” and the parameter set determined by CTE is hereafter referred to as “CTE.” 

The regional HSPF parameter approach and the original 

calibration of HSPF met the Supreme Court requirement of 

using the “best current engineering practice and scientific 

knowledge” according to the Fifth Technical Review Committee (USACE-Chicago, 2004, p ii); however, because of lack 

of documentation on the transfer, the Fifth Technical Review 

Committee recommended additional checks of simulated flows 

to confirm the accuracy of the HSPF model. An additional 

interest in parameter accuracy is the representativeness of 

runoff simulated from forestland segments. Grass and impervious lands are the primary land covers in the interior part of 

the diverted watershed but for the ungaged Calumet River and 

Lower Des Plaines River, which are located in the perimeters 

of the diverted watershed, have greater amounts of forestland 

(T.Y. Su, oral commun. September 20, 2012).

In 2008, the USGS and USACE-Chicago assessed the 

accuracy of CTE and 1999 parameter sets by evaluating the 

simulated runoff volumes from the gaged Tinley Creek and 

Midlothian Creek watersheds (USACE-Chicago, 2009). Both 

watersheds are located in the boundary of the diverted watershed (fig. 1), drain into the Calumet River system, and have 

greater than 10 percent forestland acreages. The 2008 analysis 

used meteorological and precipitation network data consistent with the LMDA model input system, and the land-cover 

features were delineated from a 2005 USGS digital orthophotography colored aerial photograph (http://seamless.usgs.

gov/, accessed March 2007; now http://geoinfo.msl.mt.gov/

Home/data/aerial_photographs_of_Montana/USGS_Digital_Orthophoto_Quarter_Quadrangles_Partners). Based on 

the simulated runoff volume to recorded streamflow volume 

(S/R) ratios, between WYs 1996 and 2005, the runoff volumes 

simulated with the 1999 parameter set underestimated the 

observed annual streamflow volumes in both watersheds. Both 

the 1999 and CTE parameter sets underestimated the observed 

annual streamflow volumes in the Tinley Creek watershed 
Hydrological Simulation Program-FORTRAN Model and Parameter Sets 7

(USACE-Chicago, 2009). Underestimation in simulated runoff 

was determined to be the result of underestimated runoff simulated from forestland, and subsequently the forestland parameters were recalibrated while keeping the grassland parameters 

unaltered from the CTE parameter set. 

The 2008 recalibration used Tinley Creek watershed as 

the index watershed and the convergence criteria of the HSPEXP expert system (Lumb and others, 1994) for evaluating 

the quality of calibration. The results indicated that the 2008 

parameter set satisfactorily improved the simulation accuracy at Tinley Creek but slightly increased the S/R ratios at 

Midlothian Creek (moved further away from unity than those 

simulated with the CTE parameter set). Because the simulation of runoff from forestland is independent of that from the 

grassland, adjusting the grassland and forestland parameters 

at the same time might be the preferred method of calibration. 

Simulation with additional gaged watersheds could be useful 

to validate the regional performance. An attempt to validate 

the parameters with the Flag Creek watershed was not successful because the Flag Creek watershed has little forestland 

coverage.

Historic Runoff Parameter Sets

Historic parameter sets and their values for the grassland 

and forestland are listed in tables 2 and 3; parameters for the 

hydraulically connected impervious land are given in table 4. 

Among the parameter sets, North Branch, Little Calumet, Des 

Plaines, and Hickory Creek are the parameter sets from the 

original calibration watersheds; CSSC are the original parameters of the Chicago Sanitary and Ship Canal (CSSC); and 

NIPC are the parameters by RUST Environment and Infrastructure (1993a, b) that were applied in the diversion accounting models by the Northeastern Illinois Planning Commission 

(NIPC). The 1999, CTE, and 2008 parameter sets have been 

explained previously in the “HSPF Model and Parameter Sets” 

section.

The 1999 parameter set differed from the NIPC parameter set in LZSN and LSUR for the forestland, and reductions 

in LSUR, SLSUR, and RETSC parameters in the impervious land segment. The CTE parameter set differed from the 

1999 parameter set in LZSN and INFILT for the grassland 

and RETSC in impervious land. The 2008 parameter set 

was developed from the CTE but recalibrated for forestland 

parameters only. The ranges of these previously calibrated 

values possibly could serve as the upper and lower limits 

(bounds) of individual parameters for the LMDA HSPF 

model, and the changes between parameter sets could reveal 

the relative importance of the parameters to the accuracy of 

LMDA simulations. It is desirable that an individual parameter value resides within its realistic bounds because perhaps 

then the predictability of the final parameter set is not biased 

when undergoing significantly different hydrological conditions from those used in the calibration. The NIPC, 1999, 

CTE, and 2008 parameter sets all have individual parameter 

values outside the ranges in earlier calibration datasets (tables 

2 and 3). These parameters (CEPSC, UZSN, LZSN, LZETP, 

INFILT, INTFW, LSUR, and KVARY for grassland; UZSN, 

LZSN, INTFW, and KVARY for forestland) affect different 

flow components of the discharge hydrographs. The reasonableness of these ranges can be assessed only if we know the 

objective function and termination criteria used at the time 

of the calibration. For a comparison, the “typical” ranges 

reported in BASINS Technical Note No. 6 (U.S. Environmental Protection Agency, 2000) are also included in tables 2 and 

3. Note that the values and ranges documented in BASINS 

Technical Note No. 6 are for typical pervious land without 

differentiating the types of land cover. It can be reasoned that 

the values for the same parameter in grassland and forestland 

should follow a consistent physical explanation; however, this 

is not consistently established in the previous calibrations. The 

performances of these parameter sets in the new data environment cannot be inferred by whether the parameter values were 

outside the reasonable ranges or not, but need be assessed 

through model testing. 

Parameters including FOREST, PETMAX, PETMIN, 

INFEXP, INFILD, and BASETP are not presented in tables 

2 and 3. These parameter definitions and the values used in 

LMDA are discussed briefly here. These parameter values 

were obtained from user control input (UCI) example files 

provided by USACE-Chicago (T.Y. Su, written commun. April 

2006). The FOREST parameter specifies the fraction of the 

land in the forestland segment that can transpire when there 

is snow (for example, conifers). Currently, FOREST is set to 

0.3 for forestland and 0.2 for grassland segments. Information 

regarding how these values were determined for the LMDA 

system was not determined during the study. It is possible that 

forestland areas were small compared to the grassland areas 

and were absorbed into the grassland for the ungaged portion of the diverted watershed; therefore, this parameter was 

assigned with higher values. This logic, however, should be 

modified if the forestland is specifically modeled. Note that 

the FOREST parameter for forestland was adjusted from 0.3 

to 0.2 in the 2008 calibration. PETMAX and PETMIN are 

the air temperatures at which plant transpiration is reduced 

to 50 percent or to 0, respectively. Current values for PETMAX and PETMIN are 50 degrees Fahrenheit (o

F) and 45 oF 

for both grassland and forestland. Using the air temperature 

record from Midway Airport, a 50 oF value for PETMAX, in 

general defines a period from October to the end of April; a 45 

o

F value for PETMIN defines a period from mid-November 

to mid-April; and a 35 oF value for PETMIN defines a period 

from mid-December to mid-February. The following three 

parameters have not been changed: INFEXP is the exponent in 

the infiltration equation, and INFILD is the ratio between the 

maximum and mean infiltration capacities over the PERLND. 

The values used in the LMDA system are the HSPF model 

default value of 2.0 as in the HSPF manual (Bicknell and 

others, 2000). BASETP is the fraction of remaining potential 
8 Lake Michigan Diversion Accounting Land Cover Change Estimation

evapotranspiration (ET) lost to riparian vegetation as base 

flow enters the streambed. The HSPF model default value 

used is 0.0. 

Reevaluation of Parameter Sets

The objectives of the study were to (1) evaluate the performance of previously developed regional parameter sets, and 

(2) develop a parameter set, if necessary, that demonstrates 

better regional performance. Evaluating HSPF simulated runoff volumes by using watersheds in or adjacent to the diverted 

Lake Michigan watershed is a viable approach to assess the 

accuracy of regional parameter sets and fits with the methodology used for the development of the original parameter sets. 

An understanding of the performance of existing parameter 

sets under the new input data environment and modifications 

to the parameters can form a basis for further improvement of 

model simulations.

Purpose and Scope

This report evaluates the accuracy of six LMDA HSPF 

parameter sets applied to nine selected watersheds in and adjacent to the diverted Lake Michigan watershed. Also, regional 

parameters representing grassland and forestland were recalibrated by manual and automatic methods and the accuracy of 

the resulting regional parameter set was evaluated. 

The premises of HSPF models were (1) use a meteorological and precipitation data system consistent with the current 

LMDA modeling practices and (2) derive land covers used in 

the LMDA-HSPF models from the National Land Cover Database 2006 (NLCD 2006, Fry and others, 2011). The accuracy 

of existing parameter sets was assessed with measured USGS 

streamflow data from WY 1996 to WY 2011. The recalibration 

adopted manual and automatic techniques and focused on the 

PERLND (grassland and forestland) parameters. SNOW and 

IMPLND parameters were not modified in this study.

Table 2. Historic rainfall-runoff parameters and values for grassland in the Hydrological Simulation Program-FORTRAN program used 

in the diversion accounting system. Data adapted from U.S. Army Corps of Engineers, Chicago District (2004), and expanded to include 

Consoer Townsend EnviroDyne Engineers parameter set.

[CSSC, Chicago Sanitary and Ship Canal; NIPC, Northeastern Illinois Planning Commission; CTE, Consoer Townsend Envirodyne Engineers; TN6, BASINS 

Technical Note 6, typical ranges. Parameter values that are outside of previously calibrated ranges in NIPC, 1999, and CTE are listed in bold]

Parameter 

name

North Branch Little Calumet Des Plaines Hickory Creek CSSC NIPC 1999 CTE TN6

CEPSC 0.12 0.1–0.2 0.12–0.15 0.15 0.1–0.12 0.25 0.25 0.1 0.03–0.20

UZSN 1.1 0.75–0.8 0.75–2.2 1.5 0.75–1.1 1.8 1.8 0.5 0.10–1.0

LZSN 7.5 8.5 7.5–8.0 8 7.5–8.5 9.5 9.5 8.5 3.0–8.0

LZETP 0.25 0.1–0.25 0.25–0.35 0.25 0.1–0.25 0.38 0.38 0.38 0.2–0.7

AGWETP 0.08 0.02–0.05 0.05–0.30 0.05 0.00–0.08 0.05 0.05 0.05 0.00–0.05

INFILT 0.015 0.02–0.022 0.015–0.045 0.02–0.03 0.015–0.02 0.015 0.015 0.1 0.01–0.10

DEEPFR 0.08 0.05–0.10 0.05–0.30 0.05 0.00–0.08 0.05 0.05 0.05 0.0–0.20

INTFW 3.5 2.7–3.2 2.5–5.0 3.5 3.2–3.5 15 15 10 1.0–3.0

LSUR 250 400 250–500 400 250–400 50 50 50 200–500

SLSUR 0.01 0.002 0.01–0.05 0.05 0.002–0.01 0.01 0.01 0.01 0.01–0.15

NSUR 0.25 0.35 0.2–0.35 0.35 0.25–0.35 0.2 0.2 0.2 0.15–0.35

IRC 0.5 0.5 0.5–0.6 0.5 0.5 0.5 0.5 0.5 0.5–0.7

KVARY 1 1.5 1.0–1.5 1 1.0–1.5 1.7 1.7 1.7 0.0–3.0

AGWRC 0.98 0.99 0.97–0.99 0.97 0.98–0.99 0.98 0.98 0.98 0.92–0.99
Hydrological Simulation Program-FORTRAN Model and Parameter Sets 9

Table 3. Historic rainfall-runoff parameter values for forestland in the Hydrological Simulation Program-FORTRAN program used in the 

diversion accounting system. Data adapted from U.S. Army Corps of Engineers, Chicago District (2004) and expanded to include Consoer 

Townsend EnviroDyne Engineers and 2008 parameter set.

[CSSC, Chicago Sanitary and Ship Canal; NIPC, Northeastern Illinois Planning Commission; CTE, Consoer Townsend Envirodyne Engineers; TN6, BASINS 

Technical Note 6, typical ranges. Parameter values that are outside of previously calibrated ranges in NIPC, 1999, and CTE are listed in bold]

Parameter 

name

North Branch Little Calumet Des Plaines Hickory Creek NIPC 1999 CTE 2008 TN6

CEPSC 0.2 0.25–0.4 0.18–0.20 0.2 0.2 0.2 0.2 0.2 0.03–0.20

UZSN 6 6 5.0–6.0 6 3 3 3 1 0.10–1.0

LZSN 7.5 8 7.5–8.0 8 10 9.5 9.5 7.5 3.0–8.0

LZETP 0.9 0.8–0.9 0.85–0.90 0.9 0.9 0.9 0.9 0.6 0.2–0.7

AGWETP 0.15 0.10–0.26 0.05–0.15 0.05 0.15 0.15 0.15 0.05 0.00–0.05

INFILT 0.007 0.01–0.025 0.005–0.015 0.005 0.01 0.01 0.01 0.005 0.01–0.10

DEEPFR 0.15 0.15–0.20 0.05–0.20 0.05 0.05 0.05 0.05 0.05 0.0–0.20

INTFW 3.5 2.5–5.0 3.0–5.0 3.5 7.5 7.5 7.5 5 1.0–3.0

LSUR 1,000 1,000 100–1,000 1,000 300 400 400 400 200–500

SLSUR 0.001 0.002 0.00–0.01 0 0.01 0.01 0.01 0.01 0.01–0.15

NSUR 0.35 0.35 0.25–0.35 0.35 0.25 0.25 0.25 0.25 0.15–0.35

IRC 0.5 0.5 0.5–0.6 0.5 0.5 0.5 0.5 0.5 0.5– -0.7

KVARY 1 1.5 1.0–1.5 1 1.7 1.7 1.7 1.5 0.0–3.0

AGWRC 0.99 0.99 0.97–0.99 0.97 0.98 0.98 0.98 0.95 0.92–0.99

Table 4. Historical rainfall-runoff parameter values for impervious land in the Hydrological 

Simulation Program-FORTRAN program used in the diversion accounting system. Data adapted from 

U.S. Army Corps of Engineers, Chicago District (2004).

[NIPC, Northeastern Illinois Planning Commission; CTE, Consoer Townsend Envirodyne Engineers; TN6, BASINS 

Technical Note 6, typical ranges; ft/ft, foot per foot. Parameter values that are outside of previously calibrated ranges in 

NIPC, 1999, and CTE are listed in bold].

Parameter 

name

Unit North 

Branch Hickory NIPC 1999 CTE 2008 TN6

LSUR feet 100 100 100 50 50 50 50–150

SLSUR ft/ft 0.015 0.015 0.015 0.01 0.01 0.01 0.01–0.05

NSUR none 0.2 0.2 0.2 0.2 0.2 0.2 0.03–0.10

RETSC inches 0.3 0.3 0.3 0.25 0.1 0.1 0.03–0.10
10 Lake Michigan Diversion Accounting Land Cover Change Estimation

Preparation of Models and Input Data

Nine gaged watersheds including Flag Creek near Willow 

Spring, IL (05533000), Skokie River near Highland Park, IL 

(05535070), Butterfield Creek near Flossmoor, IL (05536255), 

Midlothian Creek at Oak Park, IL (05536340), Tinley Creek 

near Palos Park, IL (05536500), Long Run near Lemont, IL 

(05537500), Hickory Creek at Joliet, IL (05539000), Hart 

Ditch at Dyer, IN (streamflow gage 05536179), and Hart 

Ditch at Munster, IN (05536190) representing various landuse compositions and watershed sizes, and in or adjacent to 

the diverted Lake Michigan watershed, were selected (fig. 1). 

The basic information about each watershed is summarized in 

table 5. Among the information, the “mean watershed slope” 

is similar to the HSPF runoff parameter SLSUR discussed in 

table 1. The slight variations in mean watershed slope among 

these watersheds serves as an initial indication of their hydrological similarity. The mean watershed slopes, determined 

from BSLOPGM (http://water.usgs.gov/osw/streamstats/

bcdefinitions1.html) as defined in SSURGO database (http://

soildatamart.nrcs.usda.gov, accessed September, 2013). Flood 

control reservoirs have been built in the Chicago metropolitan 

area since the 1960s (Resource Coordination Policy Committee, 1998). An “accumulative area above dams/area above 

gage” column is included to indicate the existence of reservoirs in the selected watersheds. The ratio can be greater than 

1 if there are multiple reservoirs above the gage because the 

area above each dam is counted independently. Flood control 

reservoirs - depending on the sizes, locations, and operations - 

can alter the temporal distribution of streamflows and therefore the evaluation of parameter accuracy, because the effects 

of reservoirs are not addressed in the present modeling (no 

RCHRES). Reservoirs are classified under “open water” and 

then converted as impervious surfaces in the present modeling 

work. Among the selected watersheds, Midlothian Creek had 

several flood-protection related projects constructed during the 

1970s and 1980s (Arlan Juhl, Illinois Department of Natural Resources, Office of Water Resources, written commun., 

October 2006). 

Drainage boundaries of the study watersheds were 

obtained from the Illinois StreamStats (http://water.usgs.gov/

osw/streamstats/illinois.html). The drainage boundaries of 

Hart Ditch at Dyer and at Munster, Indiana, were determined 

by compiling watershed shape files obtained from the Indiana 

StreamStats (http://water.usgs.gov/osw/streamstats/indiana.

html). The retrieved watershed boundaries might require 

modifications if they were subject to local drainage alterations. 

The National Watershed Boundary Dataset (http://nhd.usgs.

gov/wbd.html) and the boundaries of the special contributing 

area (SCA) serviced by the combined sewer system in Cook 

County, Illinois (shape files provided by USACE-Chicago District, written commun., June 2011) were reviewed for changes 

due to drainage works, sewer systems, and flood diversions. 

As a result, a small part of the Flag Creek watershed is serviced by SCA; hence, the Flag Creek watershed boundary has 

been modified.

Table 5. Background information for the selected watersheds.

[ft/ft, foot per foot; IL, Illinois; IN, Indiana]

Station 

number Station name County Area (square miles)

Mean 

watershed 

slope 

(ft/ft)

Accumulative 

area above dams/

areas above gage

Availability of daily 

streamflow records

05536340 Midlothian Creek at Oak Forest, IL Cook 13.5 0.01068 1.207 10/1/1950 to present.

05536500 Tinley Creek near Palos Park, IL Cook 11.2 0.01325 0.025 7/11/1951 to present.

05533000 Flag Creek near Willow Springs, IL Cook 16.5 0.01513 0.0 7/26/1951 to present.

05535070 Skokie River near Highland Park, IL Lake 21.1 0.00895 0.0 8/21/1967 to present.

05539000 Hickory Creek at Joliet, IL Will 107.0 0.01343 0.101 10/1/1944 to present.

05536179 Hart Ditch at Dyer Lake (IN) 37.6 0.01375 0.0 9/19/1989 to present.

05536190 Hart Ditch at Munster, IN Lake (IN) 70.7 0.01028 0.0 10/1/1942 to present.

05536255 Butterfield Creek at Flossmoor, IL Cook 23.5 0.00884 0.122 5/17/1948 to present.

05537500 Long Run near Lemont, IL Cook 20.9 0.01745 0.127 7/1/1951 to present.
Preparation of Models and Input Data 11

Determination of Land Cover

Land covers for the study watersheds were determined on 

the basis of National Land Cover Database 2006 (NLCD 2006, 

Fry and others, 2011). The NLCD uses a 16 land-use classification scheme to describe land uses/land covers; in general, 

the study watersheds and LMDA watersheds have 15 of the 16 

classes. The 15 land-use classifications then were converted to 

the grassland, forestland, and hydraulically connected impervious land categories for each watershed. The 2006 NLCD 

land-use classifications in the nine study watersheds, and the 

final grassland, forestland, and hydraulically connected land, 

in percentages, are shown in table 6. Conversion from NLCD 

land classifications to the LMDA land-cover categories is 

accomplished through a conversion matrix (table 7) developed 

in this study. Weights in table 7 are determined by calibrating 

with the 2008 results for Tinley Creek, Midlothian Creek, and 

Flag Creek that were based on the 2005 aerial photographs 

(http://seamless.usgs.gov/), and table 8 shows the comparison 

of two results at these three watersheds.

Table 6. Percentages of the Lake Michigan Diversion Accounting land-cover categories including forestland, grassland, and 

hydraulically connected impervious lands for the study watersheds and their counterpart land-cover classes according to the 

2006 National Land Cover Database.

[NLCD, National Land Cover Database; LMDA, Lake Michigan Diversion Accounting]

Watershed and station number

Midlothian 

Creek 

(05536340)

Tinley

Creek 

(05536500)

Flag Creek 

(05533000)

Skokie 

River 

(05535070)

Hickory 

Creek 

(05539000)

Hart Ditch 

at Dyer 

(05536179)

Hart Ditch 

at Munster 

(05536190)

Butterfield 

Creek 

(05536255)

Long Run 

(05537500)

Total acreage 8,076 7,197 9,196 13,380 68,794 23,639 44,447 14,801 13,513

NLCD land-cover classes (percent)

Open water 0.82 0.47 0.99 0.13 0.62 0.75 0.63 0.74 2.55

Developed open 

space

4.25 7.20 14.68 31.29 9.51 5.02 9.98 12.30 17.90

Developed low 

intensity

49.39 41.30 66.80 31.64 32.89 12.37 31.09 46.11 39.45

Developed medium 

intensity

28.91 19.44 13.88 14.99 11.79 2.06 6.78 14.45 4.99

Developed high 

intensity

3.70 3.12 2.93 7.21 2.84 0.30 2.48 3.29 0.49

Barren land (rock/

sand/clay)

0.18 0.00 0.00 0.02 0.76 0.23 0.25 0.18 0.06

Deciduous forest 3.65 11.93 0.60 7.53 5.49 15.40 9.63 3.72 8.72

Evergreen forest 0.01 0.09 0.00 0.00 0.05 0.08 0.04 0.02 0.01

Mixed forest 0.67 1.33 0.00 0.11 2.22 1.77 1.09 0.85 3.24

Shrub/scrub 0.29 1.54 0.00 0.04 1.71 0.59 0.77 0.87 0.33

Grassland/herbaceous 3.48 1.38 0.05 0.55 4.61 10.39 6.52 3.74 4.04

Pasture/hay 0.08 0.04 0.00 0.00 3.75 8.52 4.80 1.14 2.25

Cultivated crops 0.47 0.89 0.05 1.06 19.77 36.23 19.86 11.06 10.50

Woody wetlands 4.09 11.27 0.00 5.22 3.90 6.08 5.52 1.21 5.36

Emergent herbaceous 

wetlands

0.00 0.00 0.00 0.21 0.10 0.23 0.57 0.31 0.12

LMDA land-cover categories (percent)

Impervious land 36.60 28.80 37.60 31.40 23.20 7.70 19.60 29.90 23.30

Grassland 53.30 46.30 60.40 55.50 64.00 69.90 63.40 62.70 59.80

Forestland 10.10 24.90 2.00 13.10 12.80 22.40 17.00 7.40 16.90
12 Lake Michigan Diversion Accounting Land Cover Change Estimation

Table 7. Matrix for conversion of National Land Cover Database land-cover classes to 

Lake Michigan Diversion Accounting land-cover types.

[NLCD, National Land Cover Database]

NLCD 

class number NLCD class definition Impervious Grass Forest

11 Open water 1 0 0

21 Developed, open space 0.19 0.81 0

22 Developed, low intensity 0.37 0.615 0.015

23 Developed, medium intensity 0.45 0.51 0.04

24 Developed, high intensity 0.95 0.05 0

31 Barren land (rock/sand/clay) 0.75 0.25 0

41 Deciduous forest 0 0.15 0.85

42 Evergreen forest 0 0.1 0.9

43 Mixed forest 0 0.2 0.8

52 Scrub/shrub 0 0.5 0.5

71 Grassland/herbaceous 0 0.9 0.1

81 Pasture/hay 0 1 0

82 Cultivated crops 0 1 0

90 Woody wetlands 0 0 1

95 Emergent herbaceous wetlands 0 0 1

Table 8. Comparison of percentages of grassland, forestland, and hydraulically connected 

impervious land determined by 2005 aerial photographs and conversion of 

2006 National Land Cover Database land-use categories.

[NLCD, National Land Cover Database]

Source Grass 

(percent)

Forest 

(percent)

Imperviousness 

(percent)

Tinley Creek (aerial photograph) 0.46 0.24 0.3

Tinley Creek (2006 NLCD) 0.46 0.25 0.29

Midlothian Creek (aerial photograph) 0.54 0.11 0.35

Midlothian Creek (2006 NLCD) 0.53 0.1 0.37

Flag Creek (aerial photograph) 0.59 0.02 0.39

Flag Creek (2006 NLCD) 0.6 0.02 0.38
Preparation of Models and Input Data 13

Besides the acreages of each land cover for model use, 

changes in the land-cover areas in the study period could 

have hydrological effects on streamflows if the changes were 

substantial. Such information is reviewed by comparing the 

land-cover acreages determined by applying the same methodology to the retrofitted 1992 and 2001 versions of NLCD datasets (http://www.mrlc.gov/index.php); the 2011 data were not 

available before the study was conducted. The resulting percentages of forestland, grassland, and hydraulically connected 

impervious lands in each watershed are presented in table 9. 

The results indicated that the largest increase in impervious 

land percentages was from 19.6 to 23.2 percent in Hickory 

Creek watershed, followed by 27 to 29.9 percent in the Butterfield Creek watershed. The increase in percent impervious area 

in Hart Ditch, Long Run, and Midlothian Creek watersheds 

also is more than 1 percent. The increases in impervious lands 

were compensated with decreases in grassland, forestland, or 

both. The effects on the accuracy of simulation by using the 

2006 NLCD land-use data alone for the entire study period 

is examined in the “Accuracy of Regional HSPF Parameter” 

section.

Meteorological Data

Meteorological and precipitation stations used in the 

study are shown in figure 1. Data for the study period were 

provided by USACE-Chicago (T.Y. Su, USACE-Chicago, 

written commun., June 2012). Hourly meteorological data 

include air temperature, cloud cover, dewpoint temperature, 

computed potential ET, and wind speed at O’Hare Airport; air 

temperature at Midway Airport; and solar radiation at Argonne 

National Laboratory. The Illinois State Water Survey operates 

and maintains the 25-raingage precipitation network (Westcott, 

2006) and publishes hourly precipitation depth. Coordinates of 

the 25 precipitation gages were provided by Westcott (written 

commun., June 2012). Several precipitation gages previously 

have been relocated (Westcott, 2013), including a few used 

in this study. Relocations of gages used in the study were not 

appreciable, were mostly near their original sites, and occurred 

before 2011; therefore, the 2012 coordinates were used in the 

analysis. The hourly precipitation amounts were partitioned on 

the basis of the Thiessen method (Chow, 1972) and assigned 

to each study watershed and its subbasins. The meteorologic 

data were assigned according to their proximity.

Parts of the Skokie River, Hickory Creek, Hart Ditch at 

Dyer, and Hart Ditch at Munster watersheds are located outside the 25-raingage precipitation network. Precipitation data 

from the National Climatic Data Center (NCDC) (http://www.

ncdc.noaa.gov/cdo-web/, accessed June 2012) stations in the 

vicinity of these watersheds that have concurrent time periods were retrieved (fig. 1). Most of the NCDC data are daily 

stations except Crete (IL112011), which is an hourly station. 

After filling missing daily data with data from a nearby station, daily records are disaggregated to hourly by referencing 

to nearby hourly stations and using the “Disaggregate” utility 

in the WDMUtil program (Hummel and others, 2001). Station 

data that were disaggregated included Chicago Botanical Garden (station number USC00111497; “station number” is omitted hereafter), disaggregated by using ISWS raingage No. 2; 

Joliet Brandon Road Dam (IL114530), disaggregated by using 

triangular distribution; Little Red School House (IL115110), 

disaggregated by using ISWS raingages No. 11 and No. 16; 

Monee Reservoir (IL115763), disaggregated by using NWS 

Crete station; and Park Forest (IL116616), disaggregated by 

using Crete and ISWS raingage 24. Two remote stations do not 

have nearby hourly stations and the daily values of these stations were disaggregated by using the “triangular distribution” 

utility in the WDMUtil program. These stations are Romeoville Lewis University AP (IL117457) and Crown Point IN 

(IN121940). For Hickory Creek simulations, the Romeoville 

Lewis University Airport station starts from 1997. Switching 

precipitation coverage from Romeoville Lewis University 

Airport to Joliet Brandon Road Dam station did not result in 

changes in the annual S/R ratios; therefore, the disaggregated 

precipitation data at Joliet Brandon Road Dam station have 

been used for Hickory Creek simulation and the simulation 

starts from WY 1996.

Streamflow Data

Streamflow data are available at the National Water Information System (NWIS, http://waterdata.usgs.gov/il/nwis/sw/). 

For the nine study watersheds, unit values (sub-daily interval) 

and mean daily records are available. Because the length of 

daily records spans the calibration and verification periods, 

daily records were used for the study.

Daily streamflow time series and its derivatives are useful 

for describing the hydrological characteristics of a watershed; 

such information is also useful in the rainfall-runoff calibration and verification (for example, the streamflow volume in 

response to the precipitation received). The watershed yield 

characterizes a watershed, as described in the conceptual water 

balance model (eq. 2 in the “Background” section). If the 

study watersheds are hydrologically similar, the streamflow 

to precipitation responses should also be similar. Plots of the 

total monthly precipitation and normalized monthly streamflow volumes of the nine study watersheds for the study period 

are shown in figure 3. The monthly streamflow volume of 

each watershed is normalized by the corresponding drainage 

area for comparison purposes. The Flag Creek and Hart Ditch 

at Munster watersheds consistently have higher magnitudes 

of normalized monthly streamflow volumes, whereas Hart 

Ditch at Dyer and Butterfield Creek generally have lower 

normalized monthly streamflow volumes than the remaining 

study watersheds. The higher normalized monthly streamflow 

volumes in Flag Creek and Hart Ditch at Munster could be 

due to the existence of wastewater effluent discharges, which 

will be explained later in the “Effluent Discharge and Water 

Withdrawals” section. The precipitation and streamflow volumes have clear monthly distributions (fig. 3). The red line in 
Table 9. Modeled land-cover classes determined from National Land Cover Database 1992, 2001, and 2006 datasets for nine selected watersheds. [NLCD, National Land Cover Database; %, percent. Light gray shading and dark gray shading were used to highlight the increase in hydraulically connected impervious land gr

Watershed 1992 NLCD1 2001 NLCD 200

Hydraulically connected impervious land Grassland Forestland Hydraulically connected impervious land Grassland Forestland Hydraulically connected impervious land G

Midlothian Creek 35.60% 53.80% 10.50% 35.80% 54.60% 9.70% 36.60% Tinley Creek 28.60% 46.40% 25.00% 28.80% 48.80% 22.50% 28.80% Flag Creek 37.50% 60.40% 2.10% 37.50% 60.40% 2.10% 37.60% Skokie Creek 30.90% 55.40% 13.80% 30.70% 57.10% 12.20% 31.40% Hickory Creek 19.60% 66.70% 13.70% 19.60% 66.50% 13.90% 23.20% Hart Ditch–Dyer 6.90% 70.70% 22.40% 7.10% 73.00% 19.90% 7.70% Hart Ditch–Munster 18.30% 64.00% 17.60% 18.50% 66.50% 15.00% 19.60% Butterfield Creek 27.00% 64.60% 8.40% 27.00% 64.60% 8.40% 29.90% Long Run 22.10% 60.80% 17.10% 22.30% 61.90% 15.80% 23.30% 

1The land-cover classes for the 1992 dataset have been updated with the 1992/2001 Land Cover Change Retrofit Product (Fry and others, 2011).
Preparation of Models and Input Data 15

figure 3, obtained by taking the average of the nine normalized 

monthly streamflow volumes during the study period, shows 

this distribution pattern. The streamflow in October generally 

is the lowest for the area. The precipitation between November 

and March was low but streamflow gradually increased, most 

likely due to a decrease in ET. Snow pack and snowmelt are 

primary sources of streamflow in the winter months. April and 

May had the highest precipitation as well as the highest mean 

monthly streamflow in the study watersheds. Although the 

magnitudes of mean monthly precipitation remained at about 

the same level, streamflow began receding in June and reached 

the lowest point in October. 

The maximum discharge, mean discharge, minimum 

discharge, and standard deviation (table 10) are products of 

basic statistics that describe the observed streamflow time 

series. If the simulated runoff time series is reasonably correct, 

Figure 3. Normalized monthly streamflow volume (in cubic feet per second per square mile) and total monthly precipitation (in inches) 

for the nine study watersheds for the period from October 1, 1995, to September 30, 2011. The red line is the average of normalized 

monthly streamflow volume of the nine watersheds over the study period.

0

5

10

15

20

25

30

35

40

45

0 50

0.5

1

1.5

2

2.5

3

3.5

Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.

Total monthly precipitation, in inches

Normalized monthly streamflow volume, in cubic feet per second per square mile

Month

Midlothian Creek

Tinley Creek

Skokie River

Hickory Creek

EXPLANATION

Watershed

Flag Creek

Hart Ditch at Dyer

Hart Ditch at Munster

Butterfield Creek

Long Run

Average

Table 10. The maximum discharge, mean discharge, minimum discharge, and standard deviation determined from the mean daily 

discharge record for the nine study watersheds, October 1, 1995, to September 30, 2011.

[ft3/s, cubic feet per second]

Watershed

Midlothian 

Creek

Tinley 

Creek

Flag 

Creek

Skokie 

River

Hickory 

Creek

Hart Ditch 

at Dyer

Hart Ditch 

at Munster

Butterfield 

Creek

Long 

Run

Maximum discharge, 

in ft3

/s

279 817 1,040 990 5,300 2,450 3,750 1,470 2,190

Mean discharge, 

in ft3

/s

17 15 32 25 124 42 105 23 25

Minimum discharge, 

in ft3

/s

0.1 0 7.6 0 6.1 0 8.3 0 0.2

Standard deviation 30.8 39.3 47.7 52.5 272.8 104.2 224.1 64.6 63
16 Lake Michigan Diversion Accounting Land Cover Change Estimation

it should produce similar statistics. Also, the comparison of the 

maximum daily streamflows among the nine study watersheds 

indicates that the Midlothian Creek watershed has the lowest 

maximum streamflow magnitude; therefore, the operations of 

flood-protection reservoirs in the watershed have most likely 

suppressed its peaks.

Annual patterns of total precipitation and normalized 

streamflow volume for each of the nine watersheds are presented in figure 4. The red line in figure 4 shows the trend of 

normalized annual streamflow volume of the nine watersheds 

from WYs 1996 to WY 2011. The trend of annual streamflow volume is obtained by averaging the normalized annual 

streamflow volume from the nine watersheds. Based on the 

annual total precipitation amounts, WYs 2002 to 2005 were 

dry and WYs 2007 to 2011 were wet. For model calibration, 

we selected the period from WYs 2001 to 2009 for testing the 

parameters because it included dry, average, and wet years; 

and the periods of WYs 1996 to 2000 and WYs 2010 to 2011 

for model verifications. There are appreciable differences in 

the normalized annual streamflow volume among the nine 

watersheds (fig. 4). The plots of normalized annual streamflow 

volume of the nine watersheds indicate that their differences 

were smaller in wet years than in drier years. Observing these 

differences and identifying whether the differences reflect the 

nature of watersheds or external causes will help the calibration and validation processes discussed in the “Recalibration 

of PERLND Parameters” section. Flag Creek and Hart Ditch 

at Munster again have higher normalized annual streamflow 

volume and Butterfield Creek has lower normalized annual 

streamflow volume than the rest of the watersheds. The watersheds that showed alternate lower annual streamflow volume 

throughout the study period include Hart Ditch at Dyer, 

Skokie River, and Long Run. 

A flow duration curve (FDC) provides visual and a systematic means to describe the variability of flows in the specified time period at a site. The FDCs of normalized mean daily 

discharges (normalized by drainage area hence representing 

unit daily yield) for WYs 1996 to 2011 for each of the nine 

watersheds are shown in figure 5. For infrequent storm events 

(that is, storm events with high peaks or with small exceedance probabilities), the unit daily yield of the nine watersheds 

is similar except for Midlothian Creek, where storm peaks 

could be affected by operations of flood-protection reservoirs 

(see discussion for table 10). Conversely, in the low flow range 

Figure 4. Normalized annual streamflow volume (in cubic feet per second per square mile) and total monthly precipitation (in inches) 

for the nine study watersheds for the period from October 1, 1995, to September 30, 2011. The red line is the average of normalized 

annual streamflow volume of the nine watersheds over the study period.

50

100

150

200

250

300

350

400

450

0 500

0.5

1.0

1.5

2.0

2.5

3.0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Total annual precipitation, in inches

Normalized annual streamflow volume, in cubic feet per second per square mile 

Water year

0

Midlothian Creek

Tinley Creek

Skokie River

Hickory Creek

EXPLANATION

Watershed

Flag Creek

Hart Ditch at Dyer

Hart Ditch at Munster

Butterfield Creek

Long Run

Average
Preparation of Models and Input Data 17

(that is, exceedance probability close to 1), Flag Creek has 

higher magnitudes than other watersheds, followed by Hart 

Ditch at Munster, Hickory Creek, and Hart Ditch at Dyer. The 

higher low-flow magnitudes are likely augmented by wastewater treatment effluent discharge. In the low-flow range, Butterfield Creek has lower magnitudes than the other watersheds. 

The mean of daily discharge of each watershed (table 10) 

corresponds to a normalized daily discharge of approximately 

1.2 cubic feet per second per square mile (ft3/s/mi2). This mean 

value corresponds to exceedance probabilities between 0.2 and 

0.25, approximately, as is shown in figure 5. 

The measured streamflow is the sum of quick flow and 

base flow although neither of the latter two components is 

measureable. Base flow time series can be extracted from 

daily streamflow time series by using base flow separation 

techniques such as HYSEP (http://water.usgs.gov/software/

HYSEP/, accessed August 2012); however, base flow is a 

simulated time series of the watershed model. Therefore, the 

fraction of base flow in the total streamflow volume during a 

specified time period can be compared to base flow obtained 

from the simulation, and the base flow time series is useful for 

analyzing low-flow and groundwater recession rates.

In HSPF, the process parameter AGWRC controls the 

basic groundwater recession rate when KVARY is set to zero 

and groundwater does not receive inflow (Bicknell and others, 2000). Using the master-depletion curve method (Chow, 

1972), Gutierrez-Magness (2005) determined the relation 

between number of days (k coefficient) and AGWRC. For 

example, when AGWRC=0.99 or 0.97 (the maximum and 

minimum AGWRC values in tables 2 and 3), the corresponding recession days are 95 and 33 days, respectively, computed as k = –ln(AGWRC)

–1. The recession days of the study 

watersheds were examined graphically from isolated rainfallrunoff events in the study period by counting the days between 

the inflection point and the day when base flow magnitude 

stopped decreasing. For an event between April 24 and May 

15, 2007, the receding days for the nine watersheds ranged 

from 16 to 19 days, which corresponds to AGWRC values 

between 0.939 and 0.949; for an event between August 24 and 

September 12, 1999, the receding days ranged between 14 

and 18 days, and the corresponding AGWRC values ranged 

between 0.931 and 0.946.

Figure 5. Flow duration curves based on mean-daily discharge between water years 1996 and 2011 for the nine study watersheds.

0.01

0.1

1

10

100

0.001 0.01 0.1 1

Normalized mean daily discharge, in cubic feet per second per square mile

Percent of time that normalized discharge was equaled or exceeded

Midlothian Creek

Watershed

Tinley Creek

Flag Creek

Skokie River

Hickory Creek

Hart Ditch at Dyer

Hart Ditch at Munster

Butterfield Creek

Long Run

EXPLANATION
18 Lake Michigan Diversion Accounting Land Cover Change Estimation

Effluent Discharges and Water Withdrawals

Effluent discharges can come from sewer systems, 

wastewater treatment plants (WWTPs), or industrial plants 

that draw from surface-water or groundwater sources, and may 

have different paths to the surface-water system. Determining 

the role of effluents in the measured streamflow is challenging 

because the magnitudes are not readily available and the influencing factors can only be assessed by judgment. In the cases 

of effluent discharges from WWTPs and sewer overflows, or 

factory and industrial discharges whose sources are tap waters 

or operational water pumped from shallow or deep groundwater aquifers, the amounts need to be added to the simulated runoff time series. Water removed from a surface-water 

system and discharged back as effluent to the same surfacewater system at locations upstream from the gage does not 

add discharge to the observations; however, the withdrawal 

amount does need to be subtracted from the simulated runoff 

time series if effluent is returned at locations downstream from 

the gage. Further, some discharged water may have losses 

to infiltration or evaporation or others between local storage 

facilities and receiving streams.

This study retrieved historical effluent discharge data 

from various sources and treated the data as point sources with 

the magnitudes added to the simulated runoff time series in the 

corresponding watershed models. Thirty-four facilities located 

in the Butterfield Creek, Flag Creek, Hart Ditch at Dyer, Hart 

Ditch at Munster, and Long Run watersheds were identified 

through the EPA’s Permit Compliance System (PCS). The 

effluent data were retrieved from three separate databases: data 

from 1998 to 2007 were obtained from http: www.epa.gov/

enviro/facts/pcs-icis/customized.html, data from 2007 to 2010 

were obtained from http://cfpub.epa.gov/dmr/, and data from 

2007 to 2012 were obtained from http://www.epa-echo.gov/

echo/compliance_report_ water.html. After verifying the data, 

the three datasets were consolidated to form the time series 

from 1998 to 2012. Attempts to recover effluent data for 1995 

to 1997 were not successful. In this analysis, effluent magnitudes for 1995 to 1997 were estimated by using the corresponding mean monthly values determined from 1998 to 2012.

A list of the effluent discharges included in the study is 

shown in table 11. Effluent stations with magnitude less than 

0.1 million gallons per day (MGD) were omitted. Flag Creek 

has the largest effluent discharges, followed by Hickory Creek, 

Hart Ditch at Dyer, and Hart Ditch at Munster. The greater 

effluent discharges in Flag Creek and Hart Ditch at Munster 

possibly have caused higher water yield per unit area and base 

flow to streamflow ratios, and affected the shape of FDCs in 

the low-flow regime (see “Flow Duration Curves” section).

The Flagg Creek Water Reclamation District serves 10 

communities with an area of approximately 24 square miles, 

covering areas outside of the Flag Creek watershed boundary (Tracey Larmon, Flagg Creek Water Reclamation, oral 

commun., October 3, 2012). The Hinsdale sewage treatment 

plant is about 2 miles upstream from the Flag Creek streamflow gage. Effluent discharge data from the Hinsdale sewage 

treatment plant were obtained (Tracey Larmon, Flagg Creek 

Water Reclamation District, written commun., October 3, 

2012) and were the basis for estimating wastewater treatment 

facility effluent data for Flag Creek. Information for identifying the source of other effluent discharges was not directly 

available. Butterfield Creek is within the service area of Thorn 

Creek Basin Sanitary District (TCBSD). Drainage patterns 

in the watershed are complicated with sewer inflow often 

Table 11. Effluent discharge data in the study area during 1998 to 2012.

[NPDES, National Pollutant Discharge Elimination System; MGD, million gallons per day; no., number]

Watershed name and subbasin 

number where effluent discharges to NPDES permit number Mean of monthly effluent1(MGD)

Flag Creek, no. 3 IL0022586 12.18

Hickory Creek, no. 5 IL0055981 0.68

Hickory Creek, no. 6 IL0024201 1.72

Hickory Creek, no. 8 IL0020559 1.84

Hickory Creek, no. 12 IL0066613, IL0045403, 

IL0020532, IL0072192

3.44

Hickory Creek, no. 15 IL0024422 0.18

Hart Ditch at Dyer, no. 2 IL0031798 0.37

Hart Ditch at Munster, no. 16 ING080050, ING340034, 

ING340038, ING080089, 

IN0024457

9.56

Hart Ditch at Munster, no. 21 IL0031798, IN0045985 0.73

Hart Ditch at Munster, no. 36 IN0039331 1.83

1

Values in this column are the average of mean monthly effluent discharge from January 1998 to December 2012.
Accuracy of Regional Hydrological Simulation Program-FORTRAN Parameter Sets 19

overwhelming the treatment capacity of TCBSD (written commun., T.Y. Su, USACE-Chicago, June 4, 2014). The excess 

flow comes from leakage of sewer pipe (infiltration) and 

illegal sump pump connections (inflow). Inflow and infiltration seemed to be abnormally high in the Butterfield Creek 

watershed, and thus TCBSD initiated an inflow/infiltration 

limit program (T.Y. Su, USACE-Chicago, written commun., 

June 4, 2014). Once the storm runoff enters the sanitary-sewer 

system, treated or untreated water discharges to Thorn Creek, 

bypassing the USGS streamflow gage on Butterfield Creek 

(station number 05536255, not shown in the map).

Permitted water users (for example, a golf course) withdraw water directly from a stream for irrigation purposes and 

these withdrawals can affect streamflow volumes, especially 

during low-flow periods. A golf course is located in the lower 

reach of the Flag Creek watershed. The estimated average 

daily mean water use for a dry year could amount to 0.06 

MGD/day and about 0.04 MGD/day for an average year, based 

on a 6,720-yard course with 40 yards width, and 4 courses 

(Patrick Mills, USGS, oral commun., May 31, 2013). Compared to the magnitude of effluent discharges in Flag Creek, 

such withdrawal amounts are not distinguishable in measured 

discharges, and therefore are not accounted for in the model 

simulations. There are also several golf courses along Butterfield Creek and their withdrawal amounts were not estimated 

because no withdrawal records could be located (Mills, oral 

commun., May 31, 2013). 

Soil Types

Soil type is an important component in formulating 

hydrological studies. The Soil Conservation Service (SCS) 

hydrologic soil groups, which are determined by the minimum rate of infiltration obtained in bare soil after prolonged 

wetting (Natural Resources Conservation Service, 2007), are 

a set of physical data useful for estimating the initial values 

of INFILT. The dominant soil groups for the overall areas 

covering the diverted watershed and the nine study watersheds 

are C and D (fig. 6), determined from the SSURGO database 

(http://soildatamart.nrcs.usda.gov, accessed September, 2013). 

Soils in groups C and D have low to very low infiltration rates 

and therefore high runoff potentials. The soil groups for the 

nine study watersheds are primarily in C and C/D but for the 

diverted watershed (fig. 1) the predominant soil group is D. 

Groups that are classified as “C/D” in figure 6 are soils that 

are classified as group D because there is a drainage problem 

created by high water tables. However, once the soils are 

effectively drained, they are placed in group C. The INFILT 

values corresponding to the SCS soil groups are in BASINS 

Technical Note No. 6 (U.S. Environmental Protection Agency, 

2000). Among the INFILT values listed in tables 2 and 3, the 

value of 0.015 (The 1999 parameter set, table 2) belongs to 

soil group D, but a value of 0.10 (CTE parameter set, table 2) 

would belong to soil group B or C, which represents “moderate” or “moderate to high” runoff potentials.

Accuracy of Regional Hydrological 

Simulation Program-FORTRAN 

Parameter Sets Used in Lake Michigan 

Diversion Accounting

Six of the nine LMDA parameter sets were evaluated 

for their accuracy in the simulated runoff volumes. The six 

parameter sets are North Branch and Hickory Creek parameter 

sets, which represent the original calibration parameter sets; 

and NIPC, 1999, CTE, and 2008 parameter sets, which represent calibration parameter sets with known modifications. The 

Little Calumet, Des Plaines, and CSSC parameter sets reported 

multiple parameters as ranges in values and therefore were 

not feasible for the study. Each of the six parameter sets was 

applied to each of the nine HSPF watershed models, which 

generate flow component time series that can be used for 

accuracy analysis. For documenting purpose, parameter values 

of each parameter set are listed in tables 2, 3, and 4. However, 

the following parameters are not mentioned in these tables and 

are reported here. The FOREST parameters are 0.2 and 0.3 for 

grassland and forestland, respectively, for all parameter sets 

except for the 2008 parameter set, which is 0.2 for forestland; 

PETMAX and PETMIN were 50 oF and 45 oF, respectively, 

and a 0.76 factor was applied to wind input for the forestland. 

The LMDA has used the S/R ratio to describe the 

accuracy of simulated annual runoff volumes. In addition to 

annual S/R ratios, this study also used statistical and graphical techniques in the evaluation for identifying and explaining the discrepancies between simulated runoff and observed 

streamflow volumes. To evaluate the regional performance of 

a parameter set, simulation results of the nine watersheds are 

presented together. For statistical evaluations, the study used 

percent bias (PBIAS), Nash-Sutcliffe efficiency (NSE), and 

ratio of the root mean square error to the standard deviation of 

measured data (RSR), which was recommended by Moriasi 

and others (2007). The PBIAS is a measure of bias related to 

the S/R ratio as 1-S/R ratio=PBIAS. Moriasi and others (2007) 

also suggested using a performance system for rating the accuracy of model calibrations (table 12). Graphical evaluations 

used in the study include flow duration curves and doublemass curve (DMC).
20 Lake Michigan Diversion Accounting Land Cover Change Estimation

Figure 6. Soil Conservation Service hydrologic soil groups for Cook County and the nine study watersheds

col13-EMNE00-1359 Soong Figure 6

LAKE

WILL

LAKE

DUPAGE

COOK

ILLINOIS

INDIANA

Butterfield 

Creek

Hart Ditch

at Dyer

Flag Creek

Hickory

Creek

Long Run

Midlothian Creek

Hart Ditch

at Munster

Skokie

River

Tinley Creek

Lake Michigan

0 5 10 15 MILES

0 5 10 15 KILOMETERS

Base from U.S. Geological Survey,

1:100,000-scale digital data

Albers Equal-Area Conic projection

Standard parallels 45° and 33°, 

central meridian -89°

Soil Survey Geographic Database (SSURGO)

from Natural Resources Conservation

Service, U.S. Department of Agriculture

88° 87°40’ 87°20’

42°20’

42°’

41°40’

41°20’

ILLINOIS INDIANA

Map

area

EXPLANATION

Soil Survey Geographic

Database (SSURGO)

hydrologic soil group

Water

A

A/D

B

B/D

C

C/D

D

Study watershed
Accuracy of Regional Hydrological Simulation Program-FORTRAN Parameter Sets 21

The accuracy in simulated runoffs produced from 

specific land-cover categories could be a source of the 

discrepancies. For example, the Flag Creek watershed 

has the lowest amount of forestland areas (2 percent) fol

Table 12. Recommended statistical tests and ratings for evaluation of model-simulation 

Performance rating RSR NSE PBIAS

Very good 0.00≤RSR≤0.50 0.75<NSE≤1.00 PBIAS<±0.10

Good 0.50<RSR≤0.60 0.65<NSE≤0.75 ±0.10≤PBIAS<±0.15

Satistactory 0.60<RSR≤0.70 0.50<NSE≤0.65 ±0.15≤PBIAS<±0.25

Unsatisfactory 0.70<RSR NSE≤0.50 ±0.25<PBIAS

performance.

[Modified from Moriasi and others (2007). RSR, root mean square error to the standard deviation of measured data, in 

ratio; NSE, Nash-Sutcliffe efficiency; PBIAS, deviation of simulated from observed data, in ratio; ≤, less than or equal 

to; <, less than; ±, plus or minus]

Simulated to Recorded Ratios

The simulated runoff time series from a modeled watershed is the sum of direct runoff, interflow, and groundwater 

outflow time series at the outlet of the watershed. The annual 

and overall S/R ratios and the range of annual S/R ratios 

from WY 1996 to WY 2011 for the six parameter sets and 

nine watersheds are given in table 13. An overall S/R ratio, 

computed from the total volumes of the study period, is also 

included in table 13 for a simple assessment of the S/R ratio 

results. 

Based on the overall quality of the resulting annual S/R 

ratios, the three land-cover acreages determined from the 

2006 NLCD dataset are reasonably correct. The S/R ratios 

in annual and overall values in general score in the “fair” to 

“very good” ranks, but unsatisfactory underestimates occurred 

in the two Hart Ditch watersheds, and overestimates occurred 

in the Butterfield Creek, Skokie, and Long Run watersheds. 

The performance in terms of annual and overall S/R ratios is 

summarized as follows.

1. Similar results were generated from the North Branch 

and Hickory Creek parameter sets, the NIPC and 1999 

parameter sets, and the CTE and 2008 parameter sets; 

therefore, these parameter sets can be paired in three 

parameter groups. The 1999 group generated the lowest 

S/R ratios and the North Branch and 2008 groups generated “very good” results in different watersheds.

2. All parameter sets resulted in overestimation for Butterfield Creek. Overestimation also occurred for Flag 

Creek but to a smaller degree. It appeared the effluent 

discharge time series used in the simulations are reasonable; however, besides effluent discharges, unaccounted 

water withdrawals in Flag and Butterfield Creeks and 

complex infiltration and inflow losses in Butterfield 

Creek could alter the natural streamflow patterns in the 

two watersheds. 

3.

lowed by Butterfield Creek (7.4 percent). The percentage 

of land covers in each watershed is shown in table 6. The 

annual and overall S/R ratios computed for Tinley Creek, 

Hart Ditch at Dyer, and the Hart Ditch at Munster have 

underestimation patterns for all parameters except the 

2008 parameter set (table 13). These three watersheds 

have the highest forestland percentages among the nine 

study watersheds, which are 24.9, 22.4, and 17 percent, 

respectively (table 6). Long Run also has comparable 

forestland coverage (16.9 percent) but its grassland percentage is less than that of Hart Ditch at Munster (59.8 

percent compared to 63.4 percent); therefore, the S/R 

ratios for Long Run are higher.

4. The patterns of annual bias (1–annual S/R ratio) from 

watershed to watershed can be examined by plotting the 

bias time series. Examining such plots does not indicate that the annual bias corresponds consistently to the 

annual precipitation amount of a certain year, but could 

be associated with precipitation of the previous year (soil 

moisture conditions); therefore, the discrepancies are 

parameter related.

5. The North Branch parameter generated “very good” 

overall S/R ratios for Midlothian Creek, Hickory Creek, 

and Long Run watersheds, and “satisfactory” overall S/R 

ratios for all watersheds except Butterfield Creek. The 

CTE and 2008 parameter sets generated “very good” 

overall S/R ratios for Midlothian Creek, Tinley Creek, 

Hart Ditch at Dyer and Munster, and Long Run watersheds, and “satisfactory” results at all watersheds except 

Butterfield Creek. The difference between North Branch 

and CTE or 2008 is that North Branch tends to slightly 

underestimate the S/R ratios and CTE or 2008 tend to 

slightly overestimate the S/R ratios. 

As indicated in tables 2 and 3, the 1999 parameter set differed from the NIPC parameter set in LZSN and LSUR for the 

forestland, and most parameters in the impervious land segment. The CTE parameter set differed from the 1999 parameter set in LZSN and INFILT for the grassland and RETSC in -
Table 13. Ratio of simulated runoff to recorded streamflow volume (S/R ratio) for annual and total flow volumes between water years 1996 and 2011 with the six parameter sets. [NIPC, Northeastern Illinois Planning Commission; CTE, Consoer Townsend Envirodyne Engineers] Parameter set 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 

Midlothian Creek watershed North Branch 1.00 1.04 0.96 1.02 0.93 0.96 0.89 0.86 0.99 1.04 1.13 1.12 0.96 1.03 1.12 0.94 

Hickory 0.96 1.03 0.94 1.00 0.90 0.95 0.88 0.84 0.97 1.03 1.10 1.11 0.94 1.02 1.11 0.93 NIPC 0.92 0.99 0.89 0.95 0.83 0.89 0.82 0.79 0.90 0.97 1.00 1.07 0.88 0.99 1.05 0.87 1999 0.92 0.99 0.89 0.95 0.83 0.89 0.82 0.79 0.90 0.97 1.00 1.07 0.88 0.99 1.05 0.87 CTE 1.06 1.10 1.04 1.12 1.03 1.07 0.96 0.93 1.10 1.10 1.24 1.20 1.02 1.10 1.21 1.01 2008 1.09 1.10 1.04 1.11 1.03 1.06 0.95 0.93 1.10 1.08 1.24 1.20 1.02 1.09 1.21 1.01 

Tinley Creek watershed North Branch 0.78 0.95 0.85 0.77 0.75 0.80 0.71 0.72 0.72 0.89 0.82 0.86 0.94 1.05 1.15 0.94 

Hickory 0.76 0.94 0.84 0.76 0.73 0.79 0.71 0.71 0.71 0.89 0.79 0.86 0.93 1.05 1.14 0.93 NIPC 0.74 0.92 0.79 0.72 0.67 0.75 0.66 0.65 0.66 0.84 0.72 0.84 0.89 1.03 1.09 0.87 1999 0.74 0.92 0.80 0.72 0.67 0.75 0.66 0.65 0.66 0.84 0.72 0.84 0.89 1.03 1.09 0.87 CTE 0.87 1.04 0.99 0.89 0.87 0.96 0.84 0.81 0.86 1.02 0.96 1.00 1.09 1.18 1.31 1.06 2008 0.95 1.04 0.98 0.87 0.87 0.94 0.82 0.81 0.86 0.99 0.96 1.00 1.10 1.16 1.32 1.06 

Flag Creek watershed North Branch 1.15 1.23 1.23 1.06 1.06 1.13 1.11 1.14 1.17 1.18 1.19 1.13 1.22 1.17 1.12 1.26 

Hickory 1.12 1.22 1.22 1.06 1.05 1.12 1.10 1.13 1.15 1.17 1.17 1.12 1.20 1.16 1.11 1.25 NIPC 1.08 1.18 1.17 1.03 1.01 1.08 1.06 1.09 1.10 1.13 1.12 1.08 1.15 1.14 1.08 1.20 1999 1.08 1.18 1.17 1.03 1.01 1.08 1.06 1.09 1.10 1.13 1.12 1.08 1.15 1.14 1.08 1.20 CTE 1.17 1.24 1.28 1.09 1.12 1.18 1.14 1.18 1.21 1.19 1.22 1.15 1.24 1.20 1.17 1.29 2008 1.19 1.24 1.28 1.09 1.12 1.18 1.14 1.18 1.21 1.19 1.22 1.15 1.24 1.20 1.17 1.29 

Skokie River watershed

North Branch 1.23 1.70 0.92 0.92 0.91 0.99 1.09 0.93 1.10 1.32 0.96 1.24 1.35 1.12 0.98 1.38 Hickory 1.19 1.68 0.91 0.90 0.89 0.97 1.07 0.93 1.08 1.30 0.93 1.22 1.33 1.11 0.97 1.34 NIPC 1.14 1.61 0.86 0.84 0.81 0.91 1.01 0.87 1.00 1.19 0.83 1.15 1.28 1.07 0.91 1.25 1999 1.14 1.61 0.86 0.84 0.81 0.91 1.01 0.87 1.00 1.19 0.83 1.15 1.28 1.07 0.91 1.25 CTE 1.31 1.83 1.01 1.00 1.04 1.10 1.18 1.06 1.21 1.39 1.05 1.34 1.47 1.21 1.08 1.48 2008 1.36 1.83 1.00 1.00 1.03 1.10 1.18 1.04 1.21 1.38 1.04 1.35 1.47 1.20 1.08 1.49 

Hickory Creek watershed

North Branch 1.29 1.47 1.13 1.19 1.26 1.20 0.68 0.91 0.92 1.02 1.16 0.90 1.01 1.00 1.08 0.97 Hickory 1.24 1.44 1.10 1.18 1.23 1.19 0.67 0.89 0.89 1.01 1.13 0.89 0.98 0.99 1.06 0.94 NIPC 1.18 1.36 1.01 1.09 1.11 1.08 0.61 0.81 0.80 0.92 0.99 0.84 0.90 0.95 0.99 0.87 1999 1.18 1.36 1.01 1.09 1.11 1.08 0.61 0.81 0.80 0.92 0.99 0.84 0.90 0.96 0.99 0.87 CTE 1.39 1.57 1.28 1.35 1.46 1.38 0.76 0.99 1.05 1.10 1.31 0.99 1.10 1.10 1.19 1.07 2008 1.44 1.58 1.27 1.34 1.45 1.38 0.76 0.99 1.05 1.09 1.31 0.99 1.10 1.09 1.20 1.07 
Table 13. Ratio of simulated runoff to recorded streamflow volume (S/R ratio) for annual and total flow volumes between water years 1996 and 2011 

Parameter set 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 

Hart Ditch at Dyer watershed

North Branch 0.81 0.93 0.94 0.81 0.88 1.16 0.73 0.93 1.01 0.80 0.88 0.72 0.50 0.67 0.80 0.84 

Hickory 0.75 0.91 0.92 0.80 0.85 1.13 0.72 0.86 0.96 0.76 0.85 0.71 0.47 0.66 0.79 0.82 

NIPC 0.69 0.87 0.86 0.73 0.67 1.04 0.62 0.67 0.82 0.66 0.72 0.67 0.41 0.63 0.72 0.75 

1999 0.69 0.88 0.86 0.73 0.67 1.04 0.63 0.67 0.82 0.66 0.72 0.67 0.41 0.64 0.72 0.75 

CTE 0.92 1.06 1.08 0.95 1.15 1.46 0.90 1.14 1.29 0.98 1.15 0.82 0.61 0.83 0.96 0.99 

2008 1.01 1.08 1.08 0.93 1.13 1.46 0.88 1.14 1.28 0.97 1.14 0.82 0.62 0.82 0.96 0.98 

Hart Ditch at Munster watershed

North Branch 0.76 0.85 0.70 0.64 1.01 1.08 0.77 0.79 0.95 0.57 1.05 0.89 0.80 0.84 0.99 1.00 

Hickory 0.72 0.84 0.68 0.63 0.98 1.06 0.76 0.76 0.92 0.56 1.03 0.89 0.77 0.83 0.97 0.98 

NIPC 0.68 0.81 0.64 0.59 0.87 0.99 0.70 0.67 0.84 0.52 0.93 0.85 0.72 0.80 0.91 0.92 

1999 0.68 0.81 0.64 0.59 0.87 0.99 0.70 0.67 0.84 0.52 0.93 0.85 0.72 0.80 0.91 0.92 

CTE 0.82 0.94 0.77 0.72 1.16 1.26 0.86 0.89 1.08 0.64 1.23 0.97 0.88 0.94 1.11 1.12 

2008 0.87 0.94 0.77 0.71 1.15 1.26 0.85 0.90 1.08 0.63 1.22 0.97 0.89 0.92 1.11 1.12 

Butterfield Creek watershed

North Branch 1.33 1.61 1.38 1.35 1.62 1.45 1.16 1.50 1.41 1.34 1.31 1.04 1.19 1.17 1.27 1.18 

Hickory 1.27 1.58 1.34 1.34 1.58 1.42 1.14 1.46 1.36 1.33 1.28 1.03 1.16 1.16 1.24 1.15 

NIPC 1.20 1.50 1.24 1.25 1.42 1.29 1.04 1.31 1.23 1.22 1.16 0.99 1.08 1.11 1.15 1.07 

1999 1.20 1.50 1.24 1.25 1.42 1.29 1.04 1.31 1.23 1.22 1.16 0.99 1.08 1.11 1.15 1.07 

CTE 1.40 1.72 1.50 1.48 1.81 1.63 1.25 1.61 1.56 1.42 1.46 1.10 1.25 1.27 1.40 1.28 

2008 1.42 1.72 1.50 1.47 1.81 1.63 1.24 1.61 1.56 1.41 1.46 1.10 1.25 1.26 1.40 1.28 

Long Run watershed

North Branch 1.15 1.46 1.08 0.94 0.84 1.08 0.90 1.12 0.97 0.98 1.09 0.74 0.88 0.84 0.97 0.86 

Hickory 1.10 1.45 1.06 0.93 0.82 1.06 0.89 1.08 0.94 0.99 1.05 0.73 0.86 0.84 0.95 0.83 

NIPC 1.06 1.38 0.98 0.88 0.73 0.94 0.82 0.96 0.86 0.92 0.92 0.69 0.79 0.81 0.89 0.77 

1999 1.06 1.38 0.98 0.88 0.73 0.94 0.82 0.96 0.86 0.92 0.92 0.69 0.79 0.81 0.89 0.77 

CTE 1.26 1.58 1.24 1.06 1.00 1.28 1.04 1.31 1.12 1.08 1.27 0.84 0.99 0.95 1.09 0.96 

2008 1.33 1.58 1.23 1.05 0.99 1.27 1.02 1.31 1.13 1.05 1.26 0.84 0.99 0.93 1.10 0.96 

with the six parameter sets.—Continued[S, simulated annual runoff volume; R, recorded annual streamflow volume; NIPC, Northeastern Illinois Planning Commission; CTE, Consoer Townsend Envirodyne Engineers
24 Lake Michigan Diversion Accounting Land Cover Change Estimation

impervious land. The 2008 parameter set was developed based 

on CTE with recalibrated forestland parameters. Although 

the analysis pointed to the parameter values as the source of 

the discrepancies, other factors such as watershed changes or 

errors in measured meteorological or streamflow time series 

could also be the causes of simulation inaccuracy. The DMC 

and FDC analyses graphically illustrate the fit of simulated 

runoffs and the DMC also can be used to examine if other 

factors played a role in the accuracy of simulations. In the following discussions, the DMCs were developed with monthly 

observed mean streamflow and simulated runoff volumes, 

and FDCs were developed with daily mean streamflow and 

simulated runoff volumes. Unlike FDCs, time can be traced in 

the DMCs and therefore relevant changes could be gathered 

for identifying the causes of discrepancies.

Double-Mass Curve Analysis

In hydrological and meteorological studies, the DMC 

analysis has been used to determine the need for corrections to 

the data due to changes in data collection procedures or other 

local conditions (Chow, 1972). A DMC is the plot of accumulative streamflow against the accumulative precipitation 

for a specified time unit in the study period. Chaille and Yen 

(2000) applied DMC analysis to investigate historical watershed changes reflected in streamflow records representing an 

urbanizing area. If the trend in precipitation and streamflow 

yields does not change during the study period, then the DMC 

developed from the two records should follow a straight line. 

Attributes of urban developments, such as changes in land 

uses (as acreages in forestland, grassland, or impervious 

surfaces shown in table 9), storm water management (detention basins, flood mitigation controls, and others), or channel 

regulation work can introduce changes in the slopes of the 

measured DMC. Unless these changes are described in the 

models, the model simulation would not reflect the changes 

because the acreages of various land-cover types generally 

are fixed in model simulations. A change in the slope of the 

DMC will appear when the precipitation to runoff relation is 

altered. Some of the changes can be temporary (referred to as 

“slope change” in the following text), but some are permanent 

(referred to as “slope break” in the following text). 

Monthly DMCs were developed for measured streamflow 

and simulated runoff volumes. The measured DMCs are for 

examining the consistency between streamflows and precipitation, and the simulated DMCs were compared to the measured 

DMCs for evaluating the accuracy of parameters because 

the slope of simulated DMC would not change unless those 

watershed changes were introduced to the models during the 

simulations. The DMCs for the study period developed for the 

nine watersheds based on the North Branch, 1999, and 2008 

parameter sets are shown in figures 7, 8, and 9. These DMCs 

didn’t show obvious slope breaks but there were piecewise 

slope changes of various degrees in almost all measured 

DMCs (fig. 7). In figures 7, 8, and 9 the small slope changes 

in measured DMCs were matched by the simulated DMCs, 

which indicated there were no systematic problems and it is 

acceptable to use a fixed land-cover composition to model the 

rainfall-runoff mechanisms for the study period.

The North Branch parameter set underestimated streamflows at Tinley Creek, Hart Ditch at Dyer, and Hart Ditch 

at Munster; matched streamflows well at Midlothian Creek, 

Hickory Creek, and Long Run; and overestimated streamflows 

at Flag Creek and Butterfield Creek. This pattern agrees with 

the overall S/R ratios shown in table 13. The 1999 parameter 

set simulated the lowest runoff volumes in all watersheds. 

These results were achieved through parameter modifications 

including some minor changes in the IMPLND segment. The 

reduction in predicted volumes resulted in better simulation results for Skokie River and, to a lesser extent, in Flag 

Creek and Long Run. The 2008 parameter set increased the 

simulated runoff volumes from forestlands and improved the 

simulation results for Tinley Creek and Hart Ditch at Munster, 

but overestimated runoff volumes for the other watersheds. 

Simulations with the 2008 parameter set produced small slope 

changes in the observed DMCs. Overall, when a different 

parameter set was applied, there were simultaneous improvements on matches of DMCs and small slope changes at multiple watersheds, indicating that parameter values could be the 

primary cause of the simulation inaccuracy and it is possible 

that recalibrating a regional parameter set can improve the 

prediction at multiple watersheds. 

Flow Duration Curves

Analysis of FDCs can reveal how well the model reproduces the measured daily flows in the specified time period as 

well as the adequacy of simulation throughout the entire range 

of flow conditions (Van Liew and others, 2007). The FDCs 

used in the study are based on mean daily discharge data for 

the study period. Note that the model development did not 

emphasize the flow routing component; therefore, the FDCs in 

the study primarily are used for (1) assessing the general fit of 

flow magnitudes in the entire time series of the study period, 

and (2) identifying how to specify FDCs in the calibration. 

The FDCs developed from North Branch, 1999, and 2008 

parameter sets for the nine study watersheds are shown in 

figures 10, 11, and 12. Overall, the North Branch and 1999 

parameter sets underestimated flow magnitudes for the majority of the FDC frequencies (figs. 7 and 8). The underestimation 

is most prominent for high frequency quartiles (that is, low 

flows). The exception is the FDCs generated for Flag Creek by 

both parameter sets. The 2008 parameters improved the underestimations at most occurrence frequencies for Midlothian 

Creek, Tinley Creek, Skokie River, Hart Ditch at Dyer and 

Munster, and Long Run, but resulted in overestimations for 

Flag Creek, Butterfield Creek, and Hickory Creek, although 

the degree of overestimation varies.
Figure 7. Double-mass curves of observed streamflow and simulated runoff volume for the nine study watersheds using the North Branch parameter set for water years 1996 to 2011. col13-EMNE00-1359 Soong Figure 7

02468

1012

0 5 10 15 20

Cumulative runoff depth, in inches

A. Midlothian Creek

02468

10

0 5 10 15 20

B. Tinley Creek

048

121620

0 5 

C. Flag Creek

02468

10

0 5 10 15 20

Cumulative runoff depth, in inches

D. Skokie River

02468

10

0 5 10 15 20

E. Hickory Creek

02468

0 5 1

F. Hart Ditch at Dyer

02468

1012

0 5 10 15 20

Cumulative runoff depth, in inches

Cumulative precipitation depth, in inches

G. Hart Ditch at Munster

02468

10

0 5 10 15 20

Cumulative precipitation depth, in inches

H. Butterfield Creek

02468

10

0 5 1

Cumulative precipita

I. Long Run
Figure 8. Double-mass curves of observed streamflow and simulated runoff volume for the nine study watersheds using the 1999 parameter set for water years 1996 to 2011. col13-EMNE00-1359 Soong Figure 8

0 5 10 15 20

Cumulativerunoffdepth,ininches

A. Midlothian Creek

02468

10

02468

10

0 5 10 15 20

B. Tinley Creek

048

121620

0 5 1

C. Flag Creek

02468

10

0 5 10 15 20

Cumulativerunoffdepth,ininches

D. Skokie River

02468

10

02468

10

0 5 10 15 20

E. Hickory Creek F. Hart Ditch at Dyer

02468

1012

0 5 10 15 20

Cumulativerunoffdepth,ininches

Cumulative precipitation depth, in inches

G. Hart Ditch at Munster

0 5 10 15 20

Cumulative precipitation depth, in inches

H. Butterfield Creek

02468

10

0 5 1

Cumulative precipita

I. Long Run

0 5 10 

02468
Figure 9. Double-mass curves of observed streamflow and simulated runoff volume for the nine study watersheds using the 2008 parameter set for water years 1996 to 2011. col13-EMNE00-1359 Soong Figure 9

0 5 10 15 20

Cumulativerunoffdepth,ininches

A. Midlothian Creek

02468

10

0 5 10 15 20

B. Tinley Creek

048

121620

0 5 1

C. Flag Creek

0 5 10 15 20

Cumulativerunoffdepth,ininches

D. Skokie River

02468

10

0 5 10 15 20

E. Hickory Creek F. Hart Ditch at Dyer

02468

1012

0 5 10 15 20

Cumulativerunoffdepth,ininches

Cumulative precipitation depth, in inches

G. Hart Ditch at Munster

0 5 10 15 20

Cumulative precipitation depth, in inches

H. Butterfield Creek

0

2468

10

0 5 1

0 5 1

Cumulative precipita

I. Long Run

02468

1012

02468

1012

02468

10

02468
Figure 10. Flow-duration curves of observed streamflow and simulated runoff volume for the nine study watersheds using the North Branch parameter set for water years 1996 to 2011. col13-EMNE00-1359 Soong Figure 10

Dailydischarge,incubicfeetpersecond

10,000

Dailydischarge,incubicfeetpersecond

Dailydischarge,incubicfeetpersecond

Exceedance probability Exceedance probability Exceedan

1

10

100

1,000

0.1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

10

100

1,000

0.1

10

100

1,000

0 0 0.1 0.2 0.3 0.4 

1

10

100

1,000

0.1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 

1

10,000

0.1

1

10

100

1,000

1

10,000

1

10

100

1,000

10,000

0.1

10

100

1,000

1

10,000

10

100

1,000

10,000

0.1

10

100

1,000

1

A. Midlothian Creek B. Tinley Creek C. Flag Creek

D. Skokie River E. Hickory Creek F. Hart Ditch at Dyer

G. Hart Ditch at Munster H. Butterfield Creek I. Long Run
Figure 11. Flow-duration curves of observed streamflow and simulated runoff volume for the nine study watersheds 

col13-EMNE00-1359 using the 1999 parameter set for the water years 1996 to 2011. Soong Figure 11

Daily discharge, in cubic feet per second

10,000

Daily discharge, in cubic feet per second Daily discharge, in cubic feet per second

Exceedance probability Exceedance probability Exceedan

1

10

100

1,000

0.1 10

100

1,000

0.1

10

100

1,000

0 0.1 0.2 0.3 0.4 

1

10

100

1,000

0.1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 

1

10,000

10

100

1,000

10,000

0.1

10

100

1,000

1

10,000

0.1

10

100

1,000

1

A. Midlothian Creek B. Tinley Creek C. Flag Creek

D. Skokie River E. Hickory Creek F. Hart Ditch at DyerG. Hart Ditch at Munster H. Butterfield Creek I. Long Run

0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0 0.2 0.4 0.6 0.8 1

0 0.2 0.4 0.6 0.8 1

0 0.2 0.4 0.6 0.8 1

0 0.2 0.4 0.6 0.8 1

1

10

100

1,000

0

1

1

0.1

10

100

1,000
Figure 12. Flow-duration curves of observed streamflow and simulated runoff volume for the nine study watersheds using the 2008 parameter set for water years 1996 to 2011. col13-EMNE00-1359 Soong Figure 12

Dailydischarge,incubicfeetpersecond

10,000

Dailydischarge,incubicfeetpersecond

Dailydischarge,incubicfeetpersecond

Exceedance probability Exceedance probability Exceedan

1

10

100

1,000

0.1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

10

100

1,000

0.1

10

100

1,000

0 0 0.1 0.2 0.3 0.4 

1

10

100

1,000

0.1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 

1

10,000

0.1

1

10

100

1,000

1

10,000

1

10

100

1,000

10,000

0.1

10

100

1,000

1

10,000

10

100

1,000

10,000

0.1

10

100

1,000

1

A. Midlothian Creek B. Tinley Creek C. Flag Creek

D. Skokie River E. Hickory Creek F. Hart Ditch at Dyer

G. Hart Ditch at Munster H. Butterfield Creek I. Long Run
Accuracy of Regional Hydrological Simulation Program-FORTRAN Parameter Sets 31

Visually well-fitted FDCs do not imply the S/R ratio is 

always closer to unity. For example, the observed and simulated FDCs for Flag Creek, Hickory Creek, and Hart Ditch at 

Munster fit well using the North Branch parameter set (fig. 

10), but the corresponding overall S/R ratios deviated by 

approximately 16, 5, and -17 percent, respectively (table 13). 

The FDC is useful in model calibration and the fit of FDCs 

is evaluated by specifying quantiles at exceedance probability intervals important to the watershed. For example, the 

exceedance probability corresponding to the mean magnitudes 

of measured daily streamflows (table 10) is approximately 

22 percent for all of the study watersheds except for Flag 

Creek, Skokie River, Hickory Creek, and Long Run, which are 

approximately 25 percent. The exceedance probabilities for 

the mean magnitudes of simulated daily runoff were approximately between 21 and 25 percent. Therefore, the information 

content in the range of exceedance probability in this range 

needs be emphasized for volume computation when FDC is 

used as an objective function.

Quantitative Statistics

Statistics of PBIAS, NSE, and RSR based on simulated 

monthly runoff volumes by the six parameter sets are given in 

table 14. The performance of a parameter set on an individual 

watershed is evaluated by the statistics in the “very good” and 

”unsatisfactory” categories (table 12) and on nine watersheds 

by totaling the number of statistics in the “very good” and 

“unsatisfactory” categories. In table 14, results in the “very 

good” category are typed in bold and for those in the “unsatisfactory” category their cells are shaded.

Table 14. Statistics of the mean monthly runoff volume at nine study watersheds simulated by the existing parameter sets for the 

period from October 1, 1995, to September 30, 2011.

[PBIAS, percent bias; NSE, Nash-Sutcliffe model fit coefficient; RSR, ratio of the root mean square error to the standard deviation of measured data (Moriasi 

and others, 2007); NIPC, Northeastern Illinois Planning Commission; CTE, Consoer Townsend Envirodyne Engineers. Results in the “very good” category are 

shown in bold, and those in the “unsatisfactory” category are shown in gray-shaded cells]

Statistic

Watershed

Midlothian 

Creek

Tinley 

Creek

Flag 

Creek

Skokie 

Creek

Hickory 

Creek

Hart Ditch 

at Dyer

Hart Ditch 

at Munster

Butterfield 

Creek

Long 

Run

North Branch parameter set

PBIAS 0.00 -0.14 0.16 0.13 0.05 -0.20 -0.17 0.29 -0.04

NSE 0.87 0.77 0.77 0.57 0.82 0.70 0.68 0.79 0.81

RSR 0.37 0.48 0.48 0.65 0.42 0.55 0.57 0.45 0.44

Hickory Creek parameter set

PBIAS -0.02 -0.15 0.15 0.11 0.03 -0.22 -0.18 0.27 -0.05

NSE 0.87 0.77 0.80 0.62 0.84 0.70 0.67 0.83 0.82

RSR 0.35 0.48 0.45 0.61 0.40 0.55 0.58 0.41 0.43

NIPC parameter set

PBIAS -0.07 -0.19 0.11 0.04 -0.05 -0.29 -0.24 0.17 -0.13

NSE 0.86 0.74 0.84 0.65 0.82 0.64 0.63 0.87 0.79

RSR 0.38 0.51 0.40 0.59 0.42 0.60 0.61 0.36 0.46

 1999 parameter set

PBIAS -0.07 -0.19 0.11 0.04 -0.05 -0.29 -0.24 0.17 -0.13

NSE 0.86 0.74 0.84 0.65 0.82 0.64 0.63 0.87 0.79

RSR 0.38 0.51 0.40 0.59 0.42 0.60 0.61 0.36 0.46

CTE parameter set

PBIAS 0.08 -0.01 0.19 0.23 0.16 -0.03 -0.07 0.40 0.09

NSE 0.85 0.82 0.73 0.51 0.79 0.75 0.72 0.71 0.8

RSR 0.39 0.43 0.52 0.70 0.45 0.50 0.53 0.54 0.44

2008 parameter set

PBIAS 0.08 -0.01 0.19 0.23 0.16 -0.03 -0.07 0.40 0.09

NSE 0.84 0.81 0.72 0.48 0.78 0.73 0.72 0.69 0.78

RSR 0.40 0.44 0.53 0.72 0.47 0.52 0.53 0.55 0.46
32 Lake Michigan Diversion Accounting Land Cover Change Estimation

All six parameter sets produced “very good” statistics for 

Midlothian Creek. Long Run had the next best statistics with 

two “good” results in PBIAS from NIPC and 1999 parameter sets, followed by Hickory Creek with two “satisfactory” 

results in PBIAS from CTE and 2008 parameter sets. The 

NIPC and 1999 parameter sets lowered the PBIAS by approximately 10 percent except for Tinley Creek, Hart Ditch at Dyer, 

and Hart Ditch at Munster, which have higher forestland percentages, and for Flag Creek, which has a greater percentage 

of hydraulically connected impervious land percentage. The 

1999 parameter set also resulted in lower NSE and higher RSR 

in general. By modifying the grassland parameters (and further 

reducing RETSC in impervious parameters) from the 1999 

parameters, the CTE parameter set resulted in higher runoffs 

and thus increased the PBIAS for the period evaluated. Among 

the study watersheds, Flag Creek, Midlothian Creek, Skokie 

River, and Butterfield Creek have the highest impervious land 

percentages. The CTE and 2008 parameter sets yield the same 

PBIAS value; however, the 2008 parameter set had slightly 

lower NSE and higher RSR values than the CTE parameter set 

in several watersheds.

Case for Recalibration

Applying the previously calibrated LMDA regional 

parameter sets to the nine watershed models updated with 

better meteorological and land-cover data and to a time 

period different from their calibration timeframe has yielded 

results in the “very good” or “good” categories but there are 

also some in the “unsatisfactory” category. No individual 

parameter set produced “very good” or “good” results for all 

nine watersheds in the 16-year study period. The accuracy in 

simulated runoff volume could be affected by many factors, 

including the representativeness of the land cover (types and 

acreages), quality of measured data, parameterization, and the 

comprehensiveness of the model in representing the critical 

physical processes of a watershed. No potential errors were 

found in the acreages of the three land covers determined 

from the 2006 NCLD dataset or in measured precipitation 

and streamflow data. The trends in precipitation to streamflow 

responses were generally consistent in each of the nine watersheds, and the flood control reservoirs or stormwater management ordinances have not altered the streamflow patterns; 

therefore, it is reasonable to focus on parameter values for the 

simulation accuracy. Which HSPF parameters have improved 

or weakened the simulations accuracy could not be identified 

by inspecting the study results alone, but there are indications 

that the magnitude of simulation discrepancies and the proportions of grassland and forestland in the watersheds are related. 

Therefore, an attempt to recalibrate the PERLND parameters 

with the updated input data to improve the simulation accuracy of the HSPF watershed models was warranted.

The method by which a regional parameter set was determined from the original calibrations could not be determined 

(T.Y. Su, USACE-Chicago, oral commun., September 2014). 

Checking the performance of individually calibrated parameter 

sets is an acceptable approach. Evidence of improved regional 

performance has been visually observed through the DMC 

and FDC plots for the nine watersheds among the parameter 

sets tested; however, the Midlothian Creek, Hickory Creek, 

and Long Run watersheds showed tendency to respond to 

parameter sets in a similar pattern (that is, their responses in 

S/R ratios, DMCs, FDCs, and quantitative statistics moved in 

parallel). This watershed response group (that is, Midlothian 

Creek, Hickory Creek, and Long Run) was best with the North 

Branch parameter set. Similarly, the Tinley Creek and Hart 

Ditch at Munster watersheds can be grouped together and 

were best predicted with the 2008 parameter set. The Skokie 

River and to some extent the Flag Creek watersheds are the 

other group and were best represented by the 1999 parameter 

set. None of the parameter sets evaluated had dramatically 

improved results in watersheds across the watershed response 

groups. Judging by the quantitative statistics (table 14), the 

North Branch and Hickory Creek parameter sets have produced the most “satisfactory” results in the nine watersheds 

for the study period followed by the CTE and 2008 parameter 

sets. The CTE and 2008 parameter sets improved the simulation accuracy from the 1999 and NIPC parameter sets and 

achieved similar “satisfactory” results as the North Branch 

parameter set but with an “unsatisfactory” result for Butterfield Creek. In general, the NSE and RSR of CTE and 2008 

parameter set simulations were slightly inferior to those simulated by the North Branch parameter set. 

Among the nine watersheds, Flag Creek, Butterfield 

Creek, and Hart Ditch at Dyer watersheds likely are not ideal 

for recalibration purposes. Effluent discharges exist in the Flag 

Creek, Hart Ditch at Dyer, Hart Ditch at Munster, and Hickory 

Creek watersheds. Although the effluent magnitudes retrieved 

from the governmental database are reasonable, approximating 

their hourly time series from monthly values is an appreciable 

simplification. Water withdrawals in the Flag Creek watershed 

have been determined to be minimal; however, the water withdrawal amounts in the Butterfield Creek watershed are difficult 

to estimate. Furthermore, the Butterfield Creek watershed 

seemed to have complex inflow and infiltration conditions. In 

the Hart Ditch at Dyer watershed, there may be other land use 

or drainage disruptions because the piecewise slope changes in 

the measured DMC did not match the simulated DMCs. 

The Tinley Creek and Long Run watersheds were 

selected as the base watersheds for recalibrating the regional 

parameters because they represent two primary watershed 

response groups discussed above. These watersheds have no 

known effluent discharges and have more balanced distributions of forestland, grassland, and impervious land. The Tinley 

Creek and Long Run watersheds also have better raingage 

network coverage than the alternative watersheds.
Recalibration of PERLND Parameters 33

Recalibration of PERLND Parameters

The study applied both manual and automatic calibration 

techniques. In applications, the manual technique calibrated 

the Tinley Creek and Long Run watershed models separately, 

but the automatic technique calibrated the two watershed 

models separately as well as together. The purpose of the 

calibration was to compare the derived grassland and forestland parameter sets for their regional performance in simulating runoff volumes. The calibration period was from October 

1, 2000, to September 30, 2009, and the validation periods 

were from WYs 1997 to 2000 and from WYs 2010 to 2011. 

The regional performance is evaluated based on the quantitative statistics at the nine study watersheds for calibration and 

validation periods, and S/R ratios and DMCs for the entire 

study period. Note that the parameter values for the IMPLND 

segment were those specified in the CTE parameter set. 

The goal for manual calibration was to continue the 2008 

calibration work to include the grassland parameters. An automatic calibration has not previously been used in the LMDA 

system. The automatic calibration is anticipated to be robust in 

deriving an optimal parameter set but it is necessary to explore 

factors that constrain the automatic search such as the number of adjustable parameters, parameter bounds, formulation 

of objective functions, and weights. Through the automatic 

calibrations, there are opportunities to examine relevant topics 

applicable to LMDA watersheds such as: (1) parameter sensitivity, (2) applicable parameter bounds, (3) the importance of 

FOREST, PETMAX, and PETMIN, and (4) gaining insight on 

the parameter transferability for the LMDA system.

The PERLND module generally contains different landcover segments (grassland and forestland in the study); an 

order between them can be expected for a parameter of the 

same name (parameter order). The parameter order should 

follow an acceptable physical reasoning in addition to model 

calibration; for example, CEPSC for forestland would have 

higher values than for grassland. Kelliber and others (1993) 

determined that on a daily basis, the evaporation rate of a 

coniferous forest is appreciably lower than that of grassland. 

By studying soil moisture and ET of five land-cover types in 

the Loess Plateau of China during the growing season of 2011, 

Wang and others (2012) determined that the daily ET trends of 

the forests and shrub sites were similar and more stable than 

grass, subshrub, and crops (that is, corn). Incorporating the 

parameter order is a necessary part of model calibration but 

has not previously been investigated. In the existing parameter sets, a few parameters did not seem to have reasonable 

parameter orders. 

Manual Calibration

The manual calibration was conducted with the aid of an 

expert system, HSPEXP (Lumb and others, 1994). HSPEXP 

integrates expertise of experienced watershed modelers into an 

artificial intelligence structure to guide the model calibration 

through identifying the parameter(s) to be adjusted according 

to the most discrepancies in a predefined composite objective function by using a set of hierarchical rules. Users can 

tailor the acceptance criteria for each group of the composite 

objective function to meet the purpose of their study. During 

the calibration, a sequential calibration that focused a subset of 

parameters during each step was used. For calibrating lumped 

watershed models like HSPF, Lumb and others (1994) and 

Donigian (2002) recommended a strategic sequence that targets water balance, low flows, seasonal distribution, and storm 

hydrographs. 

When calibrating the Tinley Creek or Long Run watershed models, the FOREST parameter was revised to 0.1 and 

0.2 for grassland and forestland because these values were 

judged to be more representative for the LMDA system. The 

values for parameters PETMAX and PETMIN were kept the 

same as those used in the LMDA parameter sets (50 o

F and 

45 oF for PETMAX and PETMIN, respectively) because there 

was no available information to justify a change in the existing values. By targeting minimizing the differences between 

observed and simulated runoff volumes as the main objective, 

parameters LZSN, INFILT, and LZETP seemed to be most 

frequently adjusted. The INFILT value tested was within the 

bounds appropriate for SCS hydrologic soil groups C and C/D, 

as described in the BASINS Technical Note No. 6 (U.S. Environmental Protection Agency, 2000), of the study watersheds. 

Adjusting INFILT affects the lower zone soil moisture storage 

(LZSN) and ET from lower zone (LZETP), which also affects 

water available for base flow from the active groundwater 

storage zone. Base flow is determined primarily by AGWRC, 

but also can be affected by KVARY, BASETP, and DEEPFR. 

Quick flow contributes a little more of the runoff volumes than 

base flow. UZSN controls the amount of water retained in the 

upper zone that becomes available for ET. UZSN has larger 

effects on the amount of quick flow in non-winter seasons. 

Because monthly variations were not simulated, it was necessary to identify effective annual representation for UZSN. 

Hydrograph shape is affected by the relative magnitudes of the 

surface runoff and interflow; therefore, the values of INTFW, 

IRC, and CEPSC were adjusted; INTFW and IRC control the 

amount and timing of interflow. Dominant parameters other 

than those described above may be present in other watersheds 

depending on the characteristics of the watersheds (Price, 

1996). The manual calibration also incorporated the parameter 

order in adjusting parameter values for the grassland or forestland segments. Parameter order was enforced by the modeler; 

it is not expressed in HSPEXP. The convergent statistics of 

HSPEXP may not indicate the effectiveness of such adjustments and has added complexity in manual calibrations.

Calibrated parameter values and calibration results for the 

Tinley Creek and Long Run watersheds are listed in table 15. 

The errors in total volume had been minimized and each 

calibration obtained a good fit on the flow duration curves at 

most streamflow magnitudes except for Long Run at low flow. 
34 Lake Michigan Diversion Accounting Land Cover Change Estimation

Table 15. Parameter values and calibration results for the manual calibration of Tinley Creek and Long Run watershed models.

Parameter 

name

Parameter values

Tinley Creek Long Run

Grass Forest Grass Forest

FOREST 0.1 0.2 0.1 0.2

LZSN 7 7 8.5 8.5

INFILT 0.03 0.045 0.035 0.05

LSUR 50 200 50 200

SLSUR 0.01 0.01 0.01 0.01

KVARY 1.8 1.8 1.5 1.5

AGWRC 0.99 0.995 0.97 0.98

PETMAX 50 50 50 50

PETMIN 45 45 45 45

INFEXP 2 2 2 2

INFILD 2 2 2 2

DEEPFR 0.05 0.05 0.05 0.05

BASETP 0 0 0 0

AGWETP 0.05 0.05 0.02 0.05

CEPSC 0.1 0.2 0.1 0.2

UZSN 0.35 0.7 0.8 1.5

NSUR 0.2 0.25 0.2 0.25

INTFW 3 3 10 5

IRC 0.6 0.6 0.65 0.65

LZETP 0.31 0.31 0.38 0.6

Flow duration curves

Tinley Creek

Long Run

Convergence criteria

Observations to minimize Criteria

(percent)

Tinley 

Creek

(percent)

Long Run

(percent)

Error in total volume 10 0 0.3

Error in low flow recession 0.03 -0.07 -0.02

Error in 50-percent lowest flow 10 -0.4 -18.1

Error in 10-percent highest flow 15 -4.2 1.7

Error in storm peaks 20 -19.6 -7.7

Seasonal volume error 20 18.8 39.9

Summer storm volume error 50 -38.6 13.9
Recalibration of PERLND Parameters 35

Because the FOREST parameters used judgment values in the 

manual calibration, their sensitivity was investigated by setting 

the values back to 0.2 and 0.3 for grassland and forestland, 

respectively, after the calibration. Such a test resulted in minor 

reductions in simulated total runoff volume but did not cause 

violations in the convergence criteria, which is reasonable 

because the FOREST parameter affects SNOW melt portion 

in the water balance; however, if the PETMIN and PETMAX 

values were set to 35 oF for PETMIN and 45 oF for PETMAX 

in grassland and forestland, the resulting convergence criteria 

were altered enough to require continued calibration. Further 

evaluation of the FOREST, PETMAX, and PETMIN parameters will be discussed in the “Automatic Calibration” section.

Automatic Calibration

The automatic calibration was conducted by using the 

model-independent parameter estimation and uncertainty 

analysis program PEST (http://www.pesthomepage.org/). 

Examples concerning the applications of PEST to HSPF 

calibration includes Barber and others (2002) and Doherty 

and Johnston (2003). Reports that focused on PEST’s solution 

techniques with HSPF applications include Cocca and others, 

2003; Kim and others, 2006; Iskra and Droste, 2007; and 

Marce and others, 2008. The PEST user’s manual (Doherty, 

2005) and addendum (Doherty, 2013) provide detailed explanations of the theories, optimization techniques, and strategies 

for conducting parameter calibration.

The nine existing regional parameter sets provide a 

sample of local expert information about the adjustable 

parameters, initial values, and parameter bounds. The flow 

and watershed characteristics described in the “Preparation of 

Model and Input Data” section can provide auxiliary information for justifying the reasonableness of parameter bounds 

and for settling the objective function. In addition, experience 

from manual calibration can provide insight on adjustment 

increments for selective parameters. Such prior knowledge is 

useful in building the automatic calibration to meet regional 

expectations. When “expert” local information is not available or inadequate; on the other hand, users will find the initial 

values and “Typical” or “Possible” parameter ranges described 

in BASINS Technical Notes No. 6 (U.S. Environmental 

Protection Agency, 2000) useful. The composite objective 

function provided in HSPEXP is comprehensive and is ideal 

for building the objective function for PEST, especially when 

the results from the two calibrations are compared (Kim and 

others, 2006); however, a different set of objective functions 

was used in the PEST analysis and is discussed later in the 

“Objective Function” section.

Design of PEST Calibration

Using its search capabilities, it is best to program PEST 

to start the optimization with as many adjustable parameters 

and the widest bounds as possible under properly formulated 

calibration data sets (objective function). Knowing that some 

of the existing parameter sets had resulted in “very good” 

ranking in the accuracy assessment, the search to determine 

the number of adjustable parameters, parameter bounds, 

objective function, and weights for observation group needs 

to address the appropriateness of information extracted from 

existing parameter sets, and from conducting the broad search. 

Therefore, the PEST calibration was completed in two stages: 

an initial calibration and refinement. The initial calibration was 

to determine the suitable number of adjustable parameters and 

their proper bounds for the three PEST models (Tinley Creek, 

Long Run, and the Tinley Creek and Long Run, which hereafter referred as PEST Tinley Creek watershed, PEST Long Run 

watershed, and PEST two-watershed models) to conduct the 

search for a regional parameter set. In the refinement stage, the 

three PEST models subject to these constraints were then optimized by investigating parameter sensitivity, correlation, and 

the use of local expert knowledge. The initial calibration was 

completed by using a three-tier analysis as follows. The design 

of the three-tier analysis is based on the information embedded 

in existing LMDA parameter sets. 

• In Tier 1, only the grassland parameters were calibrated. Parameter values for forestland were the same 

as those in the 2008 parameter set. Adjustable parameters were those that had been modified from the original calibration through CTE and 2008 parameter sets. 

The 14 parameters included CEPSC, UZSN, LZSN, 

LZETP, AGWETP, INFILT, DEEPFR, INTFW, LSUR, 

SLSUR, NSUR, IRC, KVARY, and AGWRC. The 

bounds for these parameters follow those established in 

the “typical range” defined in BASINS Technical Note 

No. 6 (U.S. Environmental Protection Agency, 2000) 

except for those mentioned above. Initial parameter 

values for grassland parameters are based on the CTE 

parameter set.

• In Tier 2, adjustable parameters for grassland and forestland were the same 14 parameters as in Tier 1, but 

the forestland parameter was specified as a constant 

proportion of the counterpart grassland parameter 

value. The grassland parameters and corresponding 

parameter ratios were optimized. Initial parameter values for the grassland segment were the same as those 

in the 2008 parameter set. Two parameter bounds were 

tested:

a. The bounds determined in the “original” 

calibration set, and 

b. The “typical range” defined in BASINS 

Technical Note No. 6 (U.S. Environmental 

Protection Agency, 2000).
36 Lake Michigan Diversion Accounting Land Cover Change Estimation

• In Tier-3, 17 grassland and forestland parameters were 

calibrated. The initial values were the same as those of 

the 2008 parameter set, parameter ratios were applied, 

and the parameter bounds started with the “possible 

ranges” defined in BASINS Technical Note No. 6 

(U.S. Environmental Protection Agency, 2000). The 

17 parameters were FOREST, LZSN, INFILT, LSUR, 

SLSUR, KVARY, AGWRC, PETMAX, PETMIN, 

DEEPFR, AGWETP, CEPSC, UZSN, NSUR, INTFW, 

IRC, and LZETP.

During the PEST calibration, initial values for FOREST 

were 0.3 for forestland and 0.2 for grassland; PETMAX was 

50 °F and PETMIN was 45 °F, respectively, for grassland and 

forestland. Because parameter ordering was explicitly investigated (explained in a later “Parameter Ratios” section), a wind 

correction factor (0.76) specified to the forestland segment in 

the existing models was set to 1 (no correction). Additionally, 

the upper and lower bounds for INFILT were revised to 0.01 

to 0.1 inch per hour, representing soil types C to D, and for 

SLSUR were revised to vary from 0.007 to 0.02 foot per foot. 

Objective Function

The objective function of PEST, phi, is the sum of 

squared weighted residuals between models and observations 

for each of the observation groups considered. PEST adjusts 

parameters while attempting to minimize the objective function to obtain an optimum calibration. Suitable observation 

groups for forming the objective function were determined 

first and the same observation groups were kept throughout the 

calibration. The objective function used in the study consisted 

of the following observation groups: 

• Selected storm hydrographs,

• Daily streamflow,

• Daily quick flow derived from daily streamflow, 

• Daily base flow derived from daily streamflow, 

• Monthly runoff volume, 

• Annual runoff volume,

• Total runoff volume of the calibration period, and 

• Flow duration curve (FDC).

Note that this composite objective function, although 

comprehensive, is not the same as those used in HSPEXP. The 

objective function and PEST control file were set up with the 

aid of a general time-series processor to assist in model calibration and result summarization (TSPROC) program (Westenbroek and others, 2012). The storm hydrographs, quick flow 

and base flow time series, and flow volumes were determined 

using functions described in the TSPROC program. For 

specifying storm hydrographs, a threshold peak magnitude 

is needed for each watershed. The magnitude and duration 

of storm hydrographs were determined from the measured 

discharge hydrographs with a threshold peak flow discharge of 

300 ft3/s and 342 ft3/s, respectively, for Tinley Creek and Long 

Run watersheds; and the storm duration was set to 7 days 

with 2 days before the peak. Fourteen quantiles were used 

in describing an FDC with emphasis given at two tails in the 

ranges of exceedance probabilities smaller than 25 percent and 

larger than 60 percent.

Parameter Ratios

Allowing all grassland and forestland parameters to 

search some parameters independently may result in unreasonable values (for example, a parameter that should have similar 

values for grassland and forestland with values that were 

greatly different) or the problem becoming ill-posed; therefore, it enforces the need for parameter ordering. The parameter ordering was established by setting the forestland parameter equal to a ratio of the corresponding adjustable grassland 

parameter. Instead of optimizing the forestland parameters, the 

adjustable grassland parameters and the corresponding ratios 

were optimized by the PEST; therefore, the ranges for grassland parameters are evaluated and the ranges for forestland 

parameters are specified through the ratios. The initial range 

for each parameter ratio was estimated based on factors that 

affect the parameter values or based on the relations in the 

existing LMDA parameter sets. The grassland parameters were 

directly calibrated because the acreages for grassland are generally larger than those for forestland in the nine watersheds. 

The parameter ratios used in the study are as follows:

• IRC and INTFW have equal or larger values in grassland than in forestland. The range for the INTFW ratio 

used was from 0.75 to 1.0, and for a transformation 

form of IRC was 0.75–1.0.

• INFILT, LSUR, AGWETP, CEPSC, and NSUR have 

equal or larger values in forestland than in grassland. 

The range for the INFILT ratio used was 1.00–1.05, for 

the LSUR ratio was 1.0–1.5, for the AGWETP ratio 

was 1.00–1.05, for the CEPSC-ratio was 1.25–2.5, and 

for the NUSR ratio was 1.0–1.25.

• UZSN and LZETP could have much larger values in 

forestland than in grassland. The range for the UZSN 

ratio used was 1.0–2.0, and for the LZETP ratio was 

1.5–3.5.

• LZSN, SLSUR, AGWRC could have similar values in 

grassland and forestland. The range for the LZSN ratio 

used was 0.7–1.3, for the SLSUR ratio was 0.9–1.1, 

and for a transformation form of the AGWRC ratio 

was 0.95–1.05.
Recalibration of PERLND Parameters 37

Weight for Observation Groups

In PEST, the residuals between model and observations 

can be weighted, thereby allowing certain observations more 

importance than others in determining the optimization outcome to be made (Doherty, 2005). Weights for elements within 

an observation group were estimated by the inverse magnitude method (Doherty, 2005), and weights for each observation group (intergroup weights) were set so that the “phi” for 

yearly volumes, total volumes, and FDCs were approximately 

two times higher than the rest of observation groups. Initial 

PEST runs indicated that this type of weighting resulted in 

an improved calibration (higher correlation coefficients and 

smaller residuals in total volumes), compared with using uniform intergroup weights.

Initial Calibration

In the initial calibration stage, the three PEST models 

(Tinley Creek, Long Run, and PEST two-watershed) were 

applied to the three-tier designs. At this stage, a calibration run 

was considered completed if the objective function reduction 

between two consecutive runs had reached a minimum; with 

the second run using the optimal parameter values obtained 

from the prior run. Also, Doherty (2005) suggested the following indicators that the problem is not ill-posed: the Marquardt 

Lambda value must be trending smaller; the condition number 

be within proper range (less than 104, for example), and the 

order of composite parameter sensitivities within two orders 

of magnitudes. The PEST program generates detailed outputs 

for these considerations. From reviewing the recorded run 

outputs, a parameter that adhered to its upper or lower bound 

indicates that PEST was unable to determine its values based 

on the calibration datasets provided. In the initial calibration 

stage such parameter behaviors were not dealt with because 

the objective was to determine the number of adjustable 

parameters and their bounds combination. The tier to be 

used in the refinement analysis is the one that resulted in the 

most reduction in the objective function in the three PEST 

watershed models and the least residuals in the yearly runoff 

volume, total runoff volume, and FDC in the Tinley Creek and 

Long Run watersheds. Analysis of the results from the three 

tier designs are as follows.

1. For the FDC, Tier-1 yielded the least discrepancies (in 

underestimation), especially at low exceedance probabilities in two watersheds. Tier-3 had the second least 

discrepancies for Tinley Creek, but Tier-2b had the second least discrepancies for Long Run. Tier-2a produced 

the largest discrepancies (most underestimation) in most 

of the quantiles and large discrepancies in less frequent 

magnitudes. 

2. For total runoff volumes, Tier-1 yielded the smallest discrepancy (underestimation, by -0.07 percent) followed 

by Tier-3 (-0.08 percent) and Tier-2b (-0.30 percent) 

for Tinley Creek. Tier-2b had the smallest discrepancy 

(overestimated by 0.11 percent) followed by Tier-1 

(-0.47 percent) and Tier-3 (-3.82 percent) for Long Run. 

Tier-3 had the second largest discrepancies for Long Run 

(-3.83 percent). Tier-2a had the largest discrepancies 

for both watersheds, approximately -14.05 percent for 

Tinley Creek and -8.98 percent for Long Run.

3. For annual runoff volumes, Tier-3 had the most number 

of years in minimum discrepancy for the 9-year period, 

whereas Tier-2a had the least number of years of minimum discrepancy in the two watersheds from all three 

PEST watershed models. In general, the yearly discrepancy by Tier-2a analysis differed from the remaining tiers most, whereas the results of remaining tiers 

were closely tied to each other. Note that when the two 

watershed models were calibrated simultaneously (PEST 

two-watershed model), the degrees of discrepancy in 

annual runoff volumes and FDC were lower than those 

determined from individual PEST watershed models. 

These comparisons indicated that Tier-1 and Tier-3 

performed better in the Tinley Creek watershed, whereas Tier2b and Tier-1 performed better in the Long Run watershed. 

Because the objective of the study was to develop a regional 

parameter set, and Tier-1 and Tier2b had limited adjustable 

parameters and narrower bounds than those applied to Tier-3, 

the Tier-3 design was selected for further refinement.

Parameter Sensitivity 

The observation group to parameter sensitivity relations 

for the adjustable parameters in the Tier-3 analysis are listed in 

table 16 using results from the PEST Tinley Creek and PEST 

Long Run watershed models. The sensitivity relations at the 

initial level are shown in table 16. In the listing, level-1 sensitive parameters are those with the highest sensitivity, level-2 

sensitive parameters are those differed by only one order of 

magnitude, and insensitive parameters are those of the lowest 

sensitivity.

The parameters AGWRC, FOREST, and IRC were 

insensitive to the calibration data formatted for each watershed 

(table 16), and currently (2015), there is no explanation for the 

different DEEPER sensitivity between the two watersheds.

Refinement Calibration

The focus in the refinement stage was to treat insensitive 

and highly correlated parameters in the three PEST models 

and incorporate expert knowledge to assist the determination 

of these parameters. Using additional observation groups to 

form the objective function may alleviate the insensitivity of 

some parameters and could be a worthwhile effort for future 

endeavors. Insensitive parameters cannot contribute to the 

lowering of objective function, and highly correlated parameters make it difficult for PEST to differentiate which parameter 

upgrade altered the objective function. Insensitive parameters 
38 Lake Michigan Diversion Accounting Land Cover Change Estimation

Table 16. Observation group to parameter sensitivity relations obtained from the initial Tier-3 (17 parameter) analysis in PEST Tinley 

Creek and PEST Long Run watershed models.

[Two levels of sensitive parameters are listed. Level 1: most sensitive parameters, and Level 2: parameters where sensitivities differ from those of Level 1 by 

one order of magnitude. PEST, model-independent parameter estimation and uncertainty analysis program]

Observation group Most sensitive parameters Most insensitive parameters

PEST Tinley Creek watershed model

Storm hydrographs 1. INFILT, UZSN, INTFW, LZETP AGWRC

2. LZSN, INFILT, LSUR, SLSUR, KVARY, PETMAX, PETMIN, DEEPFR, 

AGWETP, UZSN, NSUR, IRC

Daily flow 1. LZSN, INFILT, LSUR, KVARY, PETMAX, PETMIN, DEEPFR, AGWETP, 

UZSN, NSUR, INTFW, IRC, LZETP

AGWRC

2. FOREST, CEPSC

Quick flow 1. LZETP, INFILT, LSUR, SLSUR, PETMAX, PETMIN, UZSN, NSUR, 

INTFW, IRC, LZETP

AGWRC

2. FOREST, KVARY, DEEPFR, AGWETP, CEPSC

Base flow 1. INFILT, AGWETP, UZSN, LZETP FOREST, AGWRC

2. LZSN, LSUR, SLSUR, KVARY, PETMAX, PETMIN, DEEPFR, CEPSC, 

NSUR, INTFW, IRC

Monthly volume 1. LZSN, INFILT, PETMAX, PETMIN, DEEPFR, AGWETP, UZSN, LZETP FOREST, AGWRC

2. LSUR, SLSUR, KVARY, CEPSC, NSUR, INTFW, IRC

Yearly volume 1. PETMAX, PETMIN, AGWETP, UZSN, LZETP AGWRC

2. LZSN, INFILT, LSUR, DEEPFR, CEPSC, NSUR, INTFW

Total volume 1. UZSN, LZETP FOREST, AGWRC, IRC

2. LZSN, INFILT, PETMAX, PETMIN, DEEPFR, AGWETP, CEPSC, INTFW

Flow duration curve 1. UZSN , LZETP FOREST, AGWRC

2. INFILT, LSUR, SLSUR, KVARY, PETMAX, PETMIN, DEEPFR, AGWETP, 

CEPSC, , NSUR, INTFW, IRC

Composite observation 1. UZSN, LZETP FOREST, AGWRC

2. LZSN, INFILT, PETMAX, PETMIN, DEEPFR, AGWETP, CEPSC, INTFW

PEST Long Run watershed model

Storm hydrographs 1. LZSN, INFILT, LSUR, PETMAX, PETMIN, UZSN, NSUR, IRC, LZETP AGWRC, DEEPFR

2. SLSUR, KVARY, AGWETP, CEPSC, INTFW

Daily flow 1. UZSN DEEPFR

2. LZSN, INFILT, LSUR, SLSUR, KVARY, PETMAX, PETMIN, AGWETP, 

NSUR, INTFW, IRC, LZETP

Quick flow 1. UZSN DEEPFR

2. LZSN, INFILT, LSUR, SLSUR, KVARY, PETMAX, PETMIN, NSUR, INTFW, IRC, LZETP

Base flow 1. INFILT, KVARY, PETMAX, PETMIN, UZSN, LZETP FOREST, DEEPFR

2. LZSN, LSUR, AGWETP, CEPSC, NSUR, INTFW, IRC

Monthly volume 1. PETMAX, PETMIN, UZSN, LZETP DEEPFR

2. LZSN, INFILT, KVARY, AGWETP

Yearly volume 1. PETMAX, PETMIN, UZSN, LZETP IRC

2. LZSN, INFILT, KVARY, AGWETP, CEPSC

Total volume 1. PETMAX, PETMIN, UZSN, LZETP IRC

2. LZSN, INFILT, AGWETP, CEPSC,

Flow duration curve 1. PETMAX, PETMIN, UZSN, LZETP FOREST, AGWRC, DEEPFR

2. LZSN, INFILT, KVARY, AGWETP, IRC

Composite observation 1. LZSN, INFILT, PETMAX, PETMIN, UZSN, LZETP AGWRC, DEEPFR

2. LSUR, SLSUR, KVARY, AGWETP, CEPSC, NSUR, INTFW, IRC
Recalibration of PERLND Parameters 39

can be assigned with generally acknowledged values that will 

not affect PEST’s optimization processes on other parameter 

values. Setting preferred values that fit the watershed characteristics as a fallback for some highly correlated parameters is 

an effective way to handle parameters with high correlations. 

Setting preferred values is done through the use of the “prior 

information” or “regularization” mode in PEST (Doherty, 

2005). PEST has also insisted on lowering LZSN to its lower 

bound and raising UZSN to its upper bound without regularization (Doherty and Johnston, 2003). Although the parameters were still calibrated for the given objective function, the 

discrepancies on low flows were appreciable. This phenomenon was met in this study even with the objective function 

used. Enforcing the regularization in the optimization process 

became an essential part of the refinement process. For parameters whose preferred values are difficult to determine, the 

BASINS Technical Note No. 6 (U.S. Environmental Protection Agency, 2000) recommended assigning these parameters 

to general preferred values and revisiting them after all other 

parameter values have been estimated.

From the results of initial calibration, weak correlations 

between the parameter and its ratio were noticed for NSUR, 

DEEPFR, SLSUR, and LSUR in the Tinley Creek watershed 

model; and for LSUR, SLSUR, and NSUR in the Long Run 

watershed model. These weak relations met physical expectations; therefore, in the refinement calibrations these parameters 

were calibrated using their grassland and forestland parameters, not the parameter ratios. Insensitive parameters could 

be identified by examining the sensitivity output, like those 

shown in table 16. Examining the correlation matrix could 

determine parameters of high correlations. Parameter pairs that 

had high correlations were LZSN and DEEPFR, INFILT and 

INTFW, INFILT and LZETP, SLSUR and NSUR, AGWRC 

and AGWETP, and KVARY and AGWRC. These parameters 

generally adhered to their upper or lower bounds at the end of 

calibration. 

Modifications made to the parameter bounds or the 

preferred values set in the refinement stage included the 

following: 

1. Modify the bounds for AGWRC to approximate the 

range obtained in the recession analysis (between 10 

and 50 days), modify the bounds for SLSUR to between 

0.0085 and 0.0015 (mean watershed slope listed in 

table 5), modify the bounds for LSUR to between 200 

and 500, and lower the upper bound of INTFW from 

10 to 5. The latter two modifications were based on flat 

terrain of the LMDA watersheds. The INFILT has been 

constrained to between 0.001 and 0.1, reflecting the soil 

of the C and D types.

2. Remove parameter ordering for NSUR, DEEPFR, 

SLSUR, and LSUR, as described above. Because that 

grassland and forestland should represent different flow 

resistance, the bounds for NSUR grassland were reduced 

to between 0.15 and 0.25. 

3. Set preferred values for insensitive parameters: 0.7 for 

IRC because of flat terrain, 0.3 for FOREST as forestland, and 0.2 for FOREST as grassland.

4. Set preferred values for AGWETP to the fraction of wetlands (0.05) obtained from the land-use analysis (table 

6) KVARY and DEEPFR to a near zero value (U.S. 

Environmental Protection Agency, 2000)

5. Set the preferred values for other grassland parameters 

(LZSN, INFILT, LSUR, SLSUR, KVARY, AGWRC, 

PETMAX, PETMIN, DEEPFR, AGWETP, CEPSC, 

UZSN, NSUR, INTFW, IRC, and LZETP) to those 

obtained from the two-watershed model under Tier-3 

constraints in the initial calibration.

The refinement stage then proceeded to calibrate the 

watershed models using PEST. After the three watershed 

models were calibrated, the KVARY, DEEPFR, and AGWETP 

mostly stayed at the values assigned. In the final step, these 

three parameters were calibrated with ET-related parameter sets to the best values that were available. At this final 

stage, non-ET-related parameters FOREST, LSUR, SLSUR, 

AGWRC, PETMAX, PETMIN, NSUR, INTFW, and LZETP 

were fixed at their optimal values. 

Analysis of Calibrated Parameter Sets

Parameter values obtained from the HSPEXP and PEST 

automatic calibrations are listed in table 17. Parameters LZSN, 

INFILT, AGWETP, CEPSC, UZSN, and LZETP were the 

most sensitive in the three PEST models; these parameters 

and their combinations form the explanatory variables of 

regression equations for those adjustable parameters (Doherty, 

2005). Each of the PEST watershed models estimated LZSN 

for grassland and forestland to be the same, raised SLUR 

to its upper bound, and lowered SLSUR to its lower bound. 

The PETMAX was lowered to 45 °F and PETMIN to a range 

between 30 °F and 35 °F. Physical representations of these 

values could be reasonable but information for justifying their 

correctness is not currently available. 

The simulated runoffs were first checked for the basic 

flow statistics (like those presented in table 10) and the fraction of base flow volume in total flow volume over the calibration period (table 18); however, keep in mind that the routing 

functions in these watershed models were preliminary approximation only (derived through BASINS) therefore matches in 

the maximum and minimum discharges are not as indicative of 

a better fit as matches in the mean discharge, reduction in variances, and the fraction of base flow in total flow volumes. For 

the Tinley Creek watershed, the PEST Tinley Creek watershed 

model performed best, followed by the PEST two-watershed 

model; for the Long Run watershed, the HSPEXP Long Run 

watershed model performed best, followed by PEST Long Run 

watershed model and PEST two-watershed model (table 18). 
40 Lake Michigan Diversion Accounting Land Cover Change Estimation

Table 17. Grassland and forestland parameters obtained from HSPEXP and PEST calibrations.

[PEST, model-independent parameter estimation and uncertainty analysis program. Note that when applying these parameter values, the wind correction factor 

for forestland should not be used.]

Parameter 

name

Grassland parameters Forestland parameters

HSPEXP calibration PEST calibration HSPEXP calibration PEST calibration

Tinley 

Creek Long Run Tinley Creek Long Run Tinley Creek and Long Run

Tinley 

Creek Long Run Tinley Creek Long Run Tinley Creek and Long Run

FOREST 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.3

LZSN 7.0 8.5 6.224 6.083 6.681 7.0 8.5 6.224 6.083 6.681

INFILT 0.03 0.035 0.023 0.04 0.032 0.045 0.05 0.024 0.044 0.033

LSUR 50 50 500 500 500 200 200 500 500 500

SLSUR 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.009 0.009 0.009

KVARY 1.8 1.5 0.517 1.441 1.240 1.8 1.5 0.846 1.491 1.377

AGWRC 0.990 0.97 0.978 0.977 0.968 0.995 0.98 0.977 0.976 0.966

PETMAX 50 50 45 45 45 50 50 45 45 45

PETMIN 45 45 35 30 30 45 45 33 35 35

INFEXP 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0

INFILD 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0

DEEPFR 0.05 0.05 0.025 0.048 0.050 0.05 0.05 0.030 0.049 0.050

BASETP 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

AGWETP 0.05 0.02 0.014 0.041 0.040 0.05 0.05 0.036 0.154 0.040

CEPSC 0.10 0.1 0.030 0.087 0.085 0.2 0.2 0.036 0.154 0.133

UZSN 0.35 0.8 0.541 0.499 1.017 0.7 1.5 1.082 0.588 1.150

NSUR 0.20 0.20 0.250 0.250 0.250 0.25 0.25 0.321 0.250 0.40

INTFW 3.0 10.0 3.752 5.0 5.0 3.0 5.0 3.375 5.0 5.0

IRC 0.60 0.65 0.500 0.620 0.531 0.6 0.65 0.500 0.685 0.621

LZETP 0.31 0.38 0.224 0.331 0.138 0.31 0.6 0.496 0.531 0.208
Recalibration of PERLND Parameters 41

Table 18. Comparison of basic flow statistics and fraction of base flow volume derived from measured daily flows and simulated daily 

runoff time series derived with the HSPEXP and PEST calibration parameters for the calibration period between October 1, 2000, and 

September 30, 2009.

[ft3/s, cubic feet per second. PEST, model-independent parameter estimation and uncertainty analysis program. Note: the minimum simulated runoff is a very 

small number (less than 0.01 cubic feet per second) and is represented as 0]

Parameter name

Maximum 

discharge 

(ft3

/s)

Minimum 

discharge 

(ft3/s)

Mean 

discharge 

(ft3/s)

Standard 

deviation

Base flow 

fraction 

Tinley Creek watershed

Measured 628 0 14.5 38.1 0.480

HSPEXP Tinley Creek watershed model 942 0 15.2 42.3 0.456

HSPEXP Long Run watershed model 859 0 13.3 33.3 0.487

PEST Tinley Creek watershed model 895 0 14.4 39.8 0.456

PEST Long Run watershed model 874 0 13.5 35.1 0.472

PEST two-watershed model 864 0 14.3 36.8 0.482

Long Run watershed

Measured 843 0.2 24.9 55.1 0.564

HSPEXP Tinley Creek watershed model 1,430 0 28.1 75.4 0.475

HSPEXP Long Run watershed model 1,310 0 24.3 56.2 0.519

PEST Tinley Creek watershed model 1,380 0.1 26.8 70.6 0.480

PEST Long Run watershed model 1,340 0 24.5 60.0 0.505

PEST two-watershed model 1,330 0 25.8 63.0 0.506

The quantitative statistics computed for the calibration 

and verification periods are listed in table 19, with “very good” 

results shown in bold and “unsatisfactory” results shown as 

shaded. Using the number of “very good” ratings on PBIAS, 

NSE, and RSR as the basis for evaluation, the PEST twowatershed and PEST Long Run watershed models performed 

best followed by the manual Long Run model in the calibration period. Apparently, the watershed selected for calibrating 

the regional parameter set is a factor; however, none of the 

parameter sets performed well for the Skokie River watershed. 

In verification period one (WYs 1997 to 2000), the performances of all parameter set dropped. The PEST two-watershed 

model performed better than the PEST-Long Run watershed 

model, followed by the HSPEXP Long Run watershed model. 

The performances of all watershed models further declined 

in validation period two (WYs 2010 and 2011), although the 

PEST two-watershed model and PEST-Long Run watershed 

model were the better performers. 

The calibrated parameters were checked for their regional 

performance with the annual and overall S/R ratios and DMCs 

based on results from the entire period WYs 1996 to 2011. 

Judging from the number of watersheds that have S/R ratios 

close to unity (table 20) and the matches of observed and simulated DMC plots (figs. 13 to 17), the parameter sets obtained 

from the PEST two-watershed model and the PEST Long Run 

watershed model perform better than other parameter sets in 

the regional applications. 
42 Lake Michigan Diversion Accounting Land Cover Change Estimation

Table 19. Quantitative statistics of PBIAS, NSE, and RSR for the calibration and verification periods for parameters obtained from 

HSPEXP and PEST calibrations.

[Calibration period: water years 2001–09; Validation period 1: water years 1997–2000; Validation period 2: water years 2000–01; PBIAS, percent bias, 

NSE, Nash-Sutcliffe efficiency; RSR, root-mean-square error to the standard deviation of measured data (Moriasi and others, 2007); PEST, model-independent 

parameter estimation and uncertainty analysis program. Results in the “very good” category are shown in bold, and those in the “unsatisfactory” category are 

shown in gray-shaded cells.]

Statistic

Midlothian 

Creek 

(percent)

Tinley Creek 

(percent)

Flag Creek 

(percent)

Skokie River 

(percent)

Hickory 

Creek 

(percent)

Hart Ditch 

at Dyer 

(percent)

Hart Ditch 

at Munster 

(percent)

Butterfield 

Creek 

(percent)

Long Run 

(percent)

HSPEXP Tinley Creek watershed model—Calibration period

PBIAS 0.14 0.05 0.22 0.31 0.14 0.05 0.01 0.43 0.13

NSE 0.77 0.73 0.65 0.24 0.73 0.64 0.72 0.59 0.69

RSR 0.48 0.52 0.59 0.87 0.52 0.6 0.53 0.64 0.56

HSPEXP Long Run watershed model—Calibration period

PBIAS 0.04 -0.08 0.17 0.18 0.01 -0.15 -0.11 0.28 -0.03

NSE 0.86 0.78 0.78 0.48 0.81 0.69 0.73 0.79 0.8

RSR 0.38 0.47 0.47 0.72 0.44 0.56 0.52 0.46 0.45

PEST Tinley Creek watershed model—Calibration period

PBIAS 0.11 -0.01 0.22 0.27 0.11 -0.02 -0.03 0.4 0.07

NSE 0.8 0.76 0.67 0.3 0.73 0.65 0.71 0.62 0.71

RSR 0.44 0.49 0.57 0.84 0.52 0.59 0.54 0.62 0.54

PEST Long Run watershed model—Calibration period

PBIAS 0.04 -0.07 0.17 0.18 0.02 -0.14 -0.11 0.29 -0.02

NSE 0.84 0.75 0.77 0.4 0.76 0.66 0.72 0.74 0.74

RSR 0.41 0.5 0.48 0.78 0.48 0.58 0.53 0.51 0.51

PEST two-watershed model—Calibration period

PBIAS 0.07 -0.01 0.18 0.22 0.06 -0.08 -0.07 0.32 0.03

NSE 0.84 0.76 0.76 0.35 0.76 0.68 0.74 0.72 0.73

RSR 0.4 0.49 0.49 0.81 0.49 0.57 0.51 0.53 0.52

HSPEXP Tinley Creek watershed model—Verification period 1

PBIAS 0.13 0 0.19 0.09 0.46 0.18 -0.1 0.67 0.2

NSE 0.69 0.76 0.56 0.65 0.43 0.76 0.62 0.21 0.60

RSR 0.55 0.49 0.66 0.59 0.75 0.49 0.62 0.89 0.63

HSPEXP Long Run watershed model—Verification period 1

PBIAS 0 -0.15 0.13 -0.05 0.25 -0.05 -0.23 0.46 0.02

NSE 0.76 0.7 0.72 0.71 0.73 0.78 0.55 0.56 0.7

RSR 0.49 0.55 0.53 0.53 0.52 0.47 0.67 0.66 0.55

PEST Tinley Creek watershed model—Verification period 1

PBIAS 0.1 -0.07 0.19 0.05 0.41 0.1 -0.14 0.63 0.14

NSE 0.71 0.73 0.58 0.66 0.52 0.77 0.57 0.29 0.63

RSR 0.54 0.52 0.65 0.58 0.69 0.48 0.66 0.84 0.61

PEST Long Run watershed model—Verification period 1

PBIAS 0.02 -0.13 0.14 -0.04 0.28 -0.04 -0.22 0.49 0.04

NSE 0.73 0.71 0.68 0.7 0.66 0.77 0.54 0.47 0.66

RSR 0.52 0.54 0.57 0.55 0.59 0.48 0.68 0.73 0.58
Recalibration of PERLND Parameters 43

Table 19. Quantitative statistics of PBIAS, NSE, and RSR for the calibration and verification periods for parameters obtained from 

HSPEXP and PEST calibrations.—Continued

[Calibration period: water years 2001–09; Validation period 1: water years 1997–2000; Validation period 2: water years 2000–01; PBIAS, percent bias, 

NSE, Nash-Sutcliffe efficiency; RSR, root-mean-square error to the standard deviation of measured data (Moriasi and others, 2007); PEST, model-independent 

parameter estimation and uncertainty analysis program. Results in the “very good” category are shown in bold, and those in the “unsatisfactory” category are 

shown in gray-shaded cells.]

Statistic

Midlothian 

Creek 

(percent)

Tinley Creek 

(percent)

Flag Creek 

(percent)

Skokie River 

(percent)

Hickory 

Creek 

(percent)

Hart Ditch 

at Dyer 

(percent)

Hart Ditch 

at Munster 

(percent)

Butterfield 

Creek 

(percent)

Long Run 

(percent)

PEST two-watershed model—Verification Period 1

PBIAS 0.04 -0.09 0.15 0 0.32 0.03 -0.18 0.52 0.08

NSE 0.76 0.74 0.68 0.67 0.68 0.78 0.57 0.49 0.69

RSR 0.49 0.51 0.56 0.57 0.57 0.47 0.66 0.72 0.55

HSPEXP Tinley Creek watershed model—Verification period 2

PBIAS 0.18 0.29 0.27 0.37 0.23 0.07 0.19 0.43 0.12

NSE 0.44 0.37 0.25 -0.47 0.61 0.58 0.36 0.23 0.63

RSR 0.75 0.79 0.87 1.21 0.63 0.65 0.8 0.88 0.61

HSPEXP Long Run watershed model—Verification period 2

PBIAS 0.07 0.12 0.20 0.22 0.08 -0.1 0.05 0.27 -0.03

NSE 0.64 0.64 0.50 0.18 0.8 0.66 0.67 0.62 0.72

RSR 0.6 0.6 0.71 0.9 0.45 0.59 0.57 0.61 0.53

PEST Tinley Creek watershed model—Verification period 2

PBIAS 0.16 0.22 0.26 0.33 0.19 0.01 0.15 0.4 0.08

NSE 0.48 0.49 0.3 -0.24 0.67 0.66 0.45 0.33 0.68

RSR 0.72 0.72 0.84 1.11 0.58 0.59 0.74 0.82 0.57

PEST Long Run watershed model—Verification period 2

PBIAS 0.09 0.16 0.22 0.24 0.1 -0.08 0.07 0.3 0

NSE 0.58 0.57 0.46 -0.02 0.76 0.65 0.6 0.56 0.69

RSR 0.65 0.65 0.74 1.01 0.49 0.59 0.63 0.67 0.56

PEST two-watershed model—Verification Period 2

PBIAS 0.11 0.2 0.22 0.27 0.13 -0.04 0.1 0.32 0.03

NSE 0.61 0.5 0.45 0.07 0.76 0.65 0.54 0.53 0.75

RSR 0.62 0.71 0.74 0.97 0.49 0.59 0.68 0.69 0.49
44 Lake Michigan Diversion Accounting Land Cover Change Estimation

Table 20. Ratio of simulated annual runoff volume to recorded annual streamflow volume (S/R ratio) determined from HSPEXP and PEST calibrations at the nine study watersheds for water years 1996 to 2011.

[S, simulated runoff volume; R, recorded streamflow volume; PEST, model-independent parameter estimation and uncertainty analysis program]

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Overall Range

Midlothian Creek

HSPEXP Tinley watershed model 1.18 1.14 1.11 1.18 1.12 1.12 1.01 1.00 1.18 1.14 1.31 1.26 1.10 1.13 1.29 1.08 1.15 (1.00–1.31)

HSPEXP Long Run watershed model 1.06 1.06 0.99 1.06 0.96 1.01 0.92 0.89 1.04 1.05 1.17 1.17 0.99 1.07 1.17 0.97 1.04 (0.88–1.17)

PEST Tinley watershed model 1.16 1.13 1.07 1.14 1.11 1.08 0.97 0.99 1.14 1.12 1.28 1.22 1.08 1.11 1.26 1.06 1.12 (0.97–1.28)

PEST Long Run watershed model 1.11 1.06 1.01 1.08 0.99 1.02 0.94 0.89 1.07 1.05 1.20 1.18 1.00 1.08 1.19 0.99 1.06 (0.89–1.2)

PEST two-watershed model 1.11 1.06 1.00 1.06 1.02 1.00 0.91 0.88 1.07 1.03 1.20 1.16 1.01 1.06 1.20 0.99 1.05 (0.88–1.2)

Tinley Creek

HSPEXP Tinley watershed model 1.01 1.12 1.07 0.95 0.98 1.02 0.90 0.90 0.95 1.07 1.05 1.08 1.20 1.22 1.42 1.16 1.08 (0.90–1.42)

HSPEXP Long Run watershed model 0.89 1.01 0.92 0.83 0.80 0.89 0.78 0.76 0.81 0.94 0.87 0.96 1.03 1.13 1.25 1.00 0.94 (0.76–1.25)

PEST Tinley watershed model 0.99 1.07 1.00 0.88 0.93 0.95 0.82 0.86 0.89 1.01 0.99 1.01 1.14 1.17 1.35 1.09 1.02 (0.82–1.35)

PEST Long Run watershed model 0.95 1.00 0.94 0.84 0.82 0.89 0.80 0.76 0.83 0.94 0.90 0.98 1.06 1.14 1.27 1.03 0.96 (0.76–1.27)

PEST two-watershed model 0.95 1.00 0.93 0.82 0.85 0.88 0.77 0.76 0.83 0.93 0.92 0.97 1.08 1.12 1.29 1.03 0.96 (0.76–1.29)

Flag Creek 

HSPEXP Tinley watershed model 1.23 1.27 1.33 1.11 1.15 1.21 1.17 1.20 1.26 1.21 1.26 1.18 1.28 1.22 1.20 1.35 1.23 (1.11–1.35)

HSPEXP Long Run watershed model 1.16 1.23 1.24 1.07 1.08 1.15 1.11 1.15 1.19 1.17 1.20 1.13 1.22 1.18 1.14 1.27 1.17 (1.07–1.27)

PEST Tinley watershed model 1.23 1.28 1.32 1.10 1.16 1.20 1.16 1.20 1.25 1.21 1.26 1.17 1.28 1.22 1.20 1.34 1.22 (1.1–1.34)

PEST Long Run watershed model 1.20 1.22 1.27 1.07 1.09 1.16 1.13 1.15 1.20 1.17 1.21 1.14 1.23 1.19 1.16 1.29 1.18 (1.07–1.29)

PEST two-watershed model 1.20 1.23 1.26 1.06 1.10 1.15 1.11 1.14 1.19 1.16 1.20 1.13 1.23 1.18 1.15 1.28 1.17 (1.06–1.28)

Skokie River 

HSPEXP Tinley watershed model 1.47 1.93 1.06 1.09 1.14 1.16 1.28 1.11 1.30 1.48 1.10 1.42 1.56 1.25 1.15 1.60 1.31 (1.06–1.93)

HSPEXP Long Run watershed model 1.31 1.75 0.95 0.96 0.95 1.04 1.13 0.99 1.15 1.32 0.98 1.30 1.41 1.16 1.03 1.42 1.17 (0.95–1.75)

PEST Tinley watershed model 1.45 1.88 1.01 1.05 1.10 1.11 1.21 1.06 1.27 1.44 1.09 1.38 1.52 1.22 1.12 1.56 1.27 (1.01–1.88)

PEST Long Run watershed model 1.39 1.77 0.96 0.97 0.98 1.06 1.16 1.00 1.18 1.32 0.99 1.32 1.44 1.19 1.05 1.45 1.20 (0.96–1.77)

PEST two-watershed model 1.39 1.75 0.93 0.97 1.00 1.04 1.14 0.94 1.17 1.31 0.98 1.32 1.44 1.16 1.05 1.46 1.19 (0.93–1.75)

Hickory Creek 

HSPEXP Tinley watershed model 1.58 1.67 1.40 1.43 1.62 1.46 0.82 1.08 1.16 1.18 1.42 1.05 1.20 1.15 1.29 1.17 1.26 (0.82–1.67)

HSPEXP Long Run watershed model 1.39 1.51 1.19 1.26 1.33 1.28 0.71 0.94 0.99 1.05 1.21 0.95 1.05 1.05 1.13 1.01 1.10 (0.71–1.51)

PEST Tinley watershed model 1.54 1.64 1.33 1.38 1.59 1.41 0.77 1.11 1.11 1.15 1.38 1.02 1.18 1.11 1.25 1.13 1.22 (0.77–1.64)

PEST Long Run watershed model 1.49 1.50 1.23 1.29 1.38 1.29 0.74 0.92 1.02 1.04 1.26 0.97 1.07 1.08 1.16 1.05 1.13 (0.74–1.5)

PEST two-watershed model 1.47 1.51 1.22 1.26 1.41 1.27 0.71 0.93 1.02 1.04 1.26 0.95 1.09 1.05 1.16 1.04 1.12 (0.71–1.51)

Hart Ditch at Dyer 

HSPEXP Tinley watershed model 1.13 1.19 1.22 1.02 1.38 1.62 0.97 1.36 1.52 1.13 1.28 0.90 0.72 0.90 1.06 1.09 1.09 (0.72–1.62)

HSPEXP Long Run watershed model 0.94 1.02 1.01 0.86 0.95 1.34 0.81 1.01 1.16 0.89 1.00 0.78 0.56 0.77 0.88 0.91 0.89 (0.55–1.34)

PEST Tinley watershed model 1.10 1.12 1.14 0.94 1.31 1.52 0.89 1.28 1.38 1.07 1.17 0.84 0.69 0.84 1.01 1.02 1.02 (0.69–1.52)

PEST Long Run watershed model 1.03 1.04 1.05 0.87 1.01 1.36 0.85 1.03 1.20 0.91 1.08 0.79 0.58 0.80 0.91 0.95 0.92 (0.58–1.36)

PEST two-watershed model 1.03 1.03 1.03 0.83 1.02 1.32 0.79 1.01 1.18 0.92 1.05 0.77 0.60 0.76 0.91 0.94 0.91 (0.60–1.32)

Hart Ditch at Munster 

HSPEXP Tinley watershed model 0.94 1.02 0.85 0.76 1.31 1.35 0.91 0.99 1.20 0.70 1.32 1.04 0.97 0.98 1.19 1.20 1.01 (0.70–1.35)

HSPEXP Long Run watershed model 0.83 0.90 0.73 0.67 1.05 1.18 0.81 0.83 1.02 0.60 1.13 0.94 0.84 0.89 1.05 1.06 0.88 (0.60–1.17)

PEST Tinley watershed model 0.93 0.98 0.81 0.73 1.28 1.29 0.87 0.96 1.13 0.68 1.25 1.00 0.95 0.95 1.16 1.15 0.97 (0.68–1.29)

PEST Long Run watershed model 0.89 0.91 0.75 0.68 1.09 1.19 0.84 0.84 1.04 0.61 1.18 0.95 0.85 0.91 1.07 1.09 0.90 (0.61–1.19)

PEST two-watershed model 0.89 0.91 0.74 0.66 1.11 1.16 0.80 0.83 1.02 0.61 1.17 0.93 0.87 0.88 1.07 1.08 0.90 (0.61–1.17)

Butterfield Creek

HSPEXP Tinley watershed model 1.56 1.80 1.62 1.54 2.01 1.70 1.33 1.76 1.70 1.52 1.55 1.17 1.35 1.32 1.49 1.38 1.50 (1.17–2.01)

HSPEXP Long Run watershed model 1.39 1.65 1.42 1.40 1.68 1.52 1.18 1.53 1.47 1.36 1.37 1.07 1.21 1.22 1.32 1.22 1.34 (1.067–1.68)

PEST Tinley watershed model 1.54 1.78 1.57 1.51 2.01 1.67 1.27 1.77 1.64 1.50 1.50 1.13 1.34 1.28 1.46 1.35 1.47 (1.13–2.01)

PEST Long Run watershed model 1.47 1.66 1.46 1.42 1.71 1.55 1.22 1.53 1.52 1.36 1.43 1.08 1.22 1.24 1.36 1.26 1.37 (1.08–1.71)

PEST two-watershed model 1.46 1.64 1.45 1.38 1.75 1.51 1.17 1.53 1.51 1.36 1.41 1.05 1.24 1.21 1.36 1.25 1.35 (1.05–1.75)

Long Run

HSPEXP Tinley watershed model 1.45 1.67 1.36 1.14 1.11 1.37 1.11 1.46 1.25 1.14 1.41 0.90 1.09 0.98 1.19 1.05 1.19 (0.90–1.67)

HSPEXP Long Run watershed model 1.26 1.51 1.15 1.00 0.90 1.17 0.96 1.19 1.06 1.01 1.14 0.80 0.94 0.90 1.04 0.91 1.03 (0.80–1.51)

PEST Tinley watershed model 1.41 1.62 1.27 1.09 1.07 1.30 1.03 1.41 1.17 1.08 1.36 0.86 1.06 0.95 1.15 1.01 1.14 (0.86–1.62)

PEST Long Run watershed model 1.35 1.51 1.19 1.01 0.93 1.18 1.00 1.22 1.10 1.01 1.19 0.81 0.96 0.92 1.06 0.93 1.05 (0.81–1.51)

PEST two-watershed model 1.35 1.51 1.18 0.99 0.95 1.17 0.95 1.22 1.08 0.98 1.20 0.80 0.98 0.89 1.07 0.93 1.05 (0.80–1.51)
col13-EMNE00-1359 Soong Figure 13

02468

1012

02468

1012

0 5 10 15 20

Cumulativerunoffdepth,ininches

A. Midlothian Creek

0 5 10 15 20

B. Tinley Creek

048

121620

0 5 1

C. Flag Creek

0 5 10 15 20

Cumulativerunoffdepth,ininches

D. Skokie River

0 5 10 15 20

E. Hickory Creek

0 5 1

F. Hart Ditch at Dyer

02468

1012

0 5 10 15 20

Cumulativerunoffdepth,ininches

Cumulative precipitation depth, in inches

G. Hart Ditch at Munster

0 5 10 15 20

Cumulative precipitation depth, in inches

H. Butterfield Creek

02468

10

02468

10

0 5 1

Cumulative precipita

I. Long Run

02468

1012

02468

1012

02468

1012

Figure 13. Double-mass curves of observed streamflow and simulated runoff volumes for the nine study watersheds using calibrated parameter set obtained from the HSPEXP Tinley Creek watershed model for water years 1996 to 2011.
Figure 14. Double-mass curves of observed streamflow and simulated runoff volumes for the nine study watersheds using calibrated parameter set obtained from the HSPEXP Long Run watershed model for water years 1996 to 2011. col13-EMNE00-1359 Soong Figure 14

02468

1012

02468

1012

0 5 10 15 20

Cumulative runoff depth, in inches

A. Midlothian Creek

02468

10

0 5 10 15 20

B. Tinley Creek

048

121620

0 5 

C. Flag Creek

0 5 10 15 20

Cumulative runoff depth, in inches

D. Skokie River

02468

10

0 5 10 15 20

E. Hickory Creek

02468

0 5 1

F. Hart Ditch at Dyer

02468

1012

0 5 10 15 20

Cumulative runoff depth, in inches

Cumulative precipitation depth, in inches

G. Hart Ditch at Munster

02468

10

0 5 10 15 20

Cumulative precipitation depth, in inches

H. Butterfield Creek

02468

10

0 5 1

Cumulative precipita

I. Long Run
Figure 15. Double-mass curves of observed streamflow and simulated runoff volumes for the nine study watersheds using calibrated parameter set obtained from the PEST Tinley Creek watershed model for water years 1996 to 2011. col13-EMNE00-1359 Soong Figure 15

02468

1012

0

48

12162024

0 5 10 15 20

Cumulative runoff depth, in inches

A. Midlothian Creek

0 5 10 15 20

B. Tinley Creek

0

2468

1012

0 5 

C. Flag Creek

0 5 10 15 20

Cumulative runoff depth, in inches

D. Skokie River

0 5 10 15 20

E. Hickory Creek F. Hart Ditch at Dyer

02468

1012

0 5 10 15 20

Cumulative runoff depth, in inches

Cumulative precipitation depth, in inches

G. Hart Ditch at Munster

0 5 10 15 20

Cumulative precipitation depth, in inches

H. Butterfield Creek

02468

10

02468

10

0 5 1

Cumulative precipita

I. Long Run

02468

1012

02468

1012

02468

1012

0 5 10 
Figure 16. Double-mass curves of observed streamflow and simulated runoff volumes for the nine study watersheds using calibrated parameter set obtained from the PEST Long Run watershed model for water years 1996 to 2011. col13-EMNE00-1359 Soong Figure 16

02468

1012

02468

1012

0 5 10 15 20

0 5 10 15 200 5 10 15 20

Cumulativerunoffdepth,ininches

A. Midlothian Creek

02468

10

B. Tinley Creek

048

121620

C. Flag Creek

Cumulativerunoffdepth,ininches

D. Skokie River

02468

10

E. Hickory Creek F. Hart Ditch at Dyer

02468

1012

Cumulativerunoffdepth,ininches

Cumulative precipitation depth, in inches

G. Hart Ditch at Munster

Cumulative precipitation depth, in inches

H. Butterfield Creek

02468

10

0

02468

10

12

2468

10

Cumulative precipita

I. Long Run

0 5 10 5 10 5 1

0 5 10 15 200 5 10 15 200 5 10 15 20
Figure 17. Double-mass curves of observed streamflow and simulated runoff volumes for the nine study watersheds using calibrated parameter set obtained from the PEST two-watershed model for water years 1996 to 2011. col13-EMNE00-1359 Soong Figure 17

02468

1012

02468

1012

Cumulativerunoffdepth,ininches

A. Midlothian Creek

02468

10

B. Tinley Creek

048

121620

C. Flag Creek

Cumulativerunoffdepth,ininches

D. Skokie River

02468

10

E. Hickory Creek

02468

F. Hart Ditch at Dyer

02468

1012

Cumulativerunoffdepth,ininches

Cumulative precipitation depth, in inches

G. Hart Ditch at Munster

02468

10

Cumulative precipitation depth, in inches

H. Butterfield Creek

02468

10

Cumulative precipita

I. Long Run

0 5 10 15 200 5 10 15 200 5 10 15 20

0 5 10 5 10 5 1

0 5 10 15 20

0 5 10 15 200 5 10 15 20
50 Lake Michigan Diversion Accounting Land Cover Change Estimation

Another criterion used to evaluate the regional performance of the parameter sets was whether a parameter set could 

break through the “watershed response group” boundaries. 

Earlier in the “Case for Recalibration” section, the “watershed 

response group” concept that subgroups of the nine study 

watersheds responded alike to certain parameter sets was discussed. Improved parameters obtained from the single watershed model did not indicate such breakthrough. For example, 

the parameter set obtained from the HSPEXP Tinley Creek 

model generated better S/R ratios, quantitative statistics, and 

DMC’s than those generated using the 2008 parameter set for 

Tinley Creek and Hart Ditch at Munster; however, improvements in these criteria in other watersheds were not as obvious. Conversely, the parameter set obtained from the PEST 

two-watershed model generated “very good” and “good” 

results in multiple watersheds that cross the boundary of 

watershed response groups. As a final check, the quantitative 

statistics pertaining to the PEST two-watershed model was 

compared to those of the North Branch parameter set (the best 

parameter set among the existing parameter sets), for the study 

period. As shown in table 21, the PEST two-watershed model 

parameter set resulted in a “very good” rating in PBIAS in five 

watersheds and the North Branch parameter set has the “very 

good” rating in three watersheds. But the performance in NSE 

and RSR by the PEST two-watershed model is not as good 

as that by the North Branch parameter set. Possible causes of 

the less ideal performance in NSE and RSR may come from 

the observation groups used to form the objective function 

or the approach, that parameters have the same values across 

watersheds, used in the present PEST two-watershed model. 

The candidate watersheds selected for use in the simultaneous 

calibration can also be an important factor for the performance 

of the resulting parameter values. These factors are not examined further in this study. 

The parameter set obtained from the PEST two-watershed 

model was selected to represent the regional parameter set 

for the nine watersheds. The grassland and forestland values 

of these parameters and how they were obtained are listed in 

table 22. Note that unless specifically specified, the bounds 

and sources used were those “possible ranges” specified in 

BASINS Technical Note No. 6 (U.S. Environmental Protection Agency, 2000). The FOREST parameters affect the HSPF 

snowmelt process. They remain unchanged in this analysis 

because SNOW parameters were not included in the calibration. Allowing some adjustable parameters to vary among the 

watersheds selected in the simultaneous multiple watershed 

model calibration can help gain insight for the parameter transferability topic. The adjustable parameters that should vary or 

not vary across watersheds depend on individual watershed 

characteristics and regional physical settings. Parameter sets 

like the North Branch or Hickory Creek that performed well 

in multiple watersheds during the evaluation analysis can be 

good candidates to include in the multiple-watershed model 

for regional parameter development.

Table 21. Quantitative statistics of PBIAS, NSE, RSR for the study periods, water years 1996 to 2011, for the North Branch 

parameter set and PEST two-watershed parameter set.

[PBIAS, percent bias; NSE, Nash-Sutcliffe efficiency; RSR, root-mean-square error to the standard deviation of measured data, Moriasi and others (2007); 

PEST, model-independent parameter estimation and uncertainty analysis program. Results in the “very good” category are shown in bold and those in the 

“unsatisfactory” category are shown in gray-shaded cells.]

Statistic

Watershed

Midlothian 

Creek 

(percent)

Tinley Creek 

(percent)

Flag Creek 

(percent)

Skokie River 

(percent)

Hickory 

Creek 

(percent)

Hart Ditch 

at Dyer 

(percent)

Hart Ditch 

at Munster 

(percent)

Butterfield 

Creek 

(percent)

Long Run 

(percent)

North Branch parameter set

PBIAS 0.00 -0.14 0.16 0.13 0.05 -0.20 -0.17 0.29 -0.04

NSE 0.87 0.77 0.77 0.57 0.82 0.70 0.68 0.79 0.81

RSR 0.37 0.48 0.48 0.65 0.42 0.55 0.57 0.45 0.44

PEST two-watershed parameter set

PBIAS 0.07 0.00 0.18 0.22 0.15 -0.04 -0.07 0.37 0.08

NSE 0.84 0.79 0.73 0.44 0.76 0.72 0.71 0.69 0.77

RSR 0.40 0.46 0.52 0.75 0.49 0.53 0.54 0.55 0.48
Recalibration of PERLND Parameters 51

Table 22. Calibrated parameter values of grassland and forestland for the nine study watersheds with description of the sources for 

estimating the parameter values.

[LMDA, Lake Michigan Diversion Accounting; in/hr, inches per hour; ft/ft, foot per foot; 1/in, inverse of an inch; ET, evapotranspiration; %, percent; 

NLCD, National Land Cover Database]

Parameter Unit Sources for estimating parameter values Grassland Forestland

DEEPFR none DEEPFR was set at a near zero value in the initial refinement calibration and then calibrated after ET related parameters were determined.

As the total volume of base flow accounts for approximately 40–50% of annual total flow 

in the study watersheds, the DEEPFR could be smaller than the existing value of 0.05.

0.05 0.05

BASETP none Default value was used.

Riparian vegetation in the nine watersheds and LMDA is not significant and has not 

changed over the years. 

Existing value of 0.0 is kept.

0.00 0.00

AGWETP none AGWETP was set at 0.05 in the initial refinement calibration stage, and then calibrated 

through PEST after the best values for ET related parameters were determined.

The NLCD results indicated the extent of marsh/wetlands in the study watersheds was approximately 0.05.

0.04 0.04

CEPSC inches Parameter values determined in the refinement calibration stage.

CEPSC varies with vegetation type/density, and land use. Forestlands have higher CEPSC 

than grasslands.

0.085 0.133

UZSN inches In the initial calibration stage UZSN adhered to the upper bound of the “possible range.” 

In the refinement calibration stage the upper bound of LZSN was modified to that of the 

“typical range,” and the final parameter values were obtained through the refinement 

calibration.

In general, forestland would have higher UZSN values than grassland. UZSN may change 

over the course of growing season.

1.017 1.15

NSUR none NSUR values adhered to the upper bound in the initial calibration stage. In the refinement calibration, the upper bound for grassland was modified to from 0.15 to 0.25, for 

forestland was modified to from 0.25 to 0.4. After the calibration in the refinement stage, 

NSUR parameters were found to stay at the upper bounds, however.

NSUR for forestland generally is higher than that of grassland.

0.25 0.4

INTFW none Parameter values determined from calibration.

For the low relief found in the nine watersheds and LMDA, the interflow should not be too 

high. The upper bound was modified to 5 from 10 as specified in the “possible range.”

5 5

IRC none IRC was set at 0.7 in the initial refinement stage and then estimated after other adjustable 

parameters have been calibrated.

IRC was highly correlated with INTFW. For the low relief of LMDA, IRC generally can be 

on the higher end.

0.531 0.621

LZETP none LZETP values were determined through the refinement calibration. 

LZETP is a function of vegetation type/density and root depth. Forestland has higher 

LZETP values than grassland.

0.138 0.208
52 Lake Michigan Diversion Accounting Land Cover Change Estimation

Summary

In this study, the accuracy of six existing rainfall-runoff 

parameter sets for the Hydrological Simulation Program-FORTRAN (HSPF) were evaluated by using nine gaged watersheds in or adjacent to the diverted Lake Michigan watershed 

in northeast Illinois and northwest Indiana. The diverted Lake 

Michigan watershed is a 673-square-mile watershed that is 

comprised of the Chicago River and Calumet River watersheds. Flows of the two rivers originally drained into Lake 

Michigan now are diverted to the Mississippi River watershed via three canals constructed in the Chicago area in the 

early twentieth century. The U.S. Army Corps of Engineers, 

Chicago District has applied the HSPF to estimate the runoff 

from the ungaged portion, which is about 393 square miles, of 

the diverted Lake Michigan watershed. All HSPF watershed 

models in the system use the same parameter set to simulate 

their runoff; therefore, assessing and documenting the accuracy of this “regional parameter set” is an important element 

of the Lake Michigan Diversion Accounting (LMDA) system. 

The nine gaged watersheds comprised Flag Creek near Willow 

Spring, IL, Skokie River near Highland Park, IL, Butterfield 

Creek near Flossmoor, IL, Midlothian Creek at Oak Park, IL, 

Tinley Creek near Palos Park, IL, Long Run near Lemont, IL, 

Hickory Creek at Joliet, IL, Hart Ditch at Dyer, IN, and Hart 

Ditch at Munster, IN. Nine regional parameter sets have been 

developed and used in the past or currently are in use by the 

LMDA system. In this study, the HSPF models for the nine 

watersheds were established using the three land-cover types 

(grass, forest, and hydraulically connected imperviousness) 

that were determined using the 2006 National Land Cover 

Database, and driven by the latest meteorological and precipitation data consistent with the current (2014) LMDA system 

modeling framework. Each of the six parameter sets, comprising the North Branch, Hickory Creek, NIPC, 1999, CTE, and 

2008, was applied to the nine watershed models to simulate 

runoff from water years 1996 to 2011.

The goal of the parameter set is to focus on the accuracy 

of modeled runoff volumes for water budget predictions, and a 

better parameter set is the one that can attain a better regional 

performance, that is, to have “very good” rating according to 

the criteria used in more watersheds. The evaluation criteria 

used included: on annual basis, the simulated runoff volume 

to recorded streamflow volume (S/R) ratio; on monthly time 

interval, the goodness-of-fit statistics percent of bias (PBIAS), 

Nash-Sutcliffe efficiency (NSE), and root mean square error to 

the standard deviation of measured data (RSR), and visual fit 

of simulated and observed double mass curve (DMC). Before 

evaluating the six existing parameter sets, an analysis of input 

data excluded possible errors in measured data and inaccuracy caused by effects from urban development in the nine 

watersheds; in particular it was determined that it is reasonable 

to use one set of land-cover descriptions in each watershed 

model for the 16 years of simulation. The following conclusions are drawn from the parameter evaluation.

1. The North Branch and Hickory Creek parameter sets 

that belong to the original calibration group attained 

an overall “satisfactory” rating in the simulated runoff 

volume based on three performance statistics, but none 

of the existing parameter sets produced “very good” or 

“good” results for all nine watersheds in the 16-year 

study period. 

2. Judging from their performance in the nine watersheds, 

the six evaluated parameter sets were placed into three 

groups such that each parameter set group had better 

simulation results for a subgroup of the nine watersheds. The North Branch and Hickory Creek parameter 

sets comprise one parameter set group, and the runoff 

volumes of Midlothian Creek, Hickory Creek, and Long 

Run watersheds were best predicted by the North Branch 

parameter set. The CTE and 2008 comprise another 

parameter set group, and the runoff of Tinley Creek and 

Hart Ditch at Munster watersheds were best predicted 

by the 2008 parameter set. Lastly, the NIPC and 1999 

parameter sets comprise the third parameter set group 

and the runoff of Skokie and Flag Creek watersheds 

were best predicted by the 1999 parameter set. Breaking 

the boundaries of these “watershed response groups” is a 

criterion for the regional performance evaluation.

3. After excluding the possible errors in input data and 

land-cover changes, the simulation errors are considered 

to be parameter related. Through the parameter accuracy 

evaluation, there is evidence of improvements in accuracy that occurred in different groups of the nine study 

watersheds by different parameter sets; therefore, it is 

feasible to recalibrate the parameters to attain a better 

performing regional parameter set.

Recalibration focused on parameters in the PERLND 

(pervious land) segment of HSPF, and used both a manual 

calibration method with the aid of the HSPEXP program 

(Lumb and others, 1994) and an automatic calibration method 

with the aid of the model-independent parameter estimation 

and uncertainty analysis program PEST program. The objective of the parameter recalibration was to develop a regional 

parameter set that can achieve more accurate runoff volume 

simulation for the nine study watersheds, and therefore for 

the ungaged Lake Michigan Diversion Accounting (LMDA) 

watershed area. The recalibration did not include parameters 

pertinent to the snow simulation, nor monthly variations for 

parameters that could have seasonal effects: that is, only 

annual values of parameters were considered.

The recalibration used Tinley Creek and Long Run as 

the representative watersheds for testing and developing 

the regional parameter set. The calibration period was from 

October 1, 2000, to September 30, 2009, and the validation 

periods were from WYs 1997 to 2000 and from WYs 2010 

to 2011. HSPEXP was applied to calibrate the parameters in 

each watershed model individually, but PEST was applied 

to calibrate the parameters in watershed models individually 
Summary 53

and together, that is, a PEST Tinley Creek watershed model, a 

PEST Long Run watershed model, and a PEST two-watershed 

model were developed. Because the existing parameter sets 

may contain information valuable to the automatic calibration pertaining to the upper and lower bounds for adjustable 

parameters, what parameters can be considered as adjustable, 

and likely values for parameters relating to watershed characteristics; and because PEST has not been applied to the LMDA 

system before, the calibrations with PEST were carried out in 

two stages: the initial calibration and refinement calibration 

stages. The goal of the initial calibration was to determine the 

suitable number of adjustable parameters and their bounds for 

the three PEST watershed models. Besides those determined 

from the existing parameter sets, parameter bounds from the 

“typical range” and “possible range” specified in BASINS 

Technical Note No. 6 were also evaluated in the initial calibration, and the objective function for each PEST model consisted of multiple observation groups including: selected storm 

hydrographs, daily streamflow, daily quick flows and base 

flows, monthly, annual, and total runoff volumes, and flow 

duration curves. All three PEST watershed models started with 

initial parameter values specified as in the 2008 parameter set. 

A PEST watershed model calibration was considered completed if reduction in objective function between two consecutive runs reached a minimum and the model run remained 

stable. The calibration was the one that generated the most 

accurate predictions in all three PEST watershed models.

The initial PEST calibration results indicated that 

parameter bounds adapted from the historical parameter sets 

restricted PEST’s ability to search for optimal parameter 

combinations. From the initial calibration the parameter set 

selected to move on to the refinement calibration, was the one 

that produced best matches in the flow duration curve, annual 

and total runoff volumes. The refinement calibration started 

with all regular adjustable parameters (17 parameters in grassland and in forestland) with their bounds set according to the 

“possible range” specified in BASINS Technical Note No. 6.

In the final step, calibrated parameter sets from the two 

HSPEXP and three PEST watershed models were compared 

for their accuracy and regional performance as follows.

• Flow statistics of simulated and observed mean and 

standard deviation of daily streamflows, and fraction 

of base flow to total flow volumes for the calibration 

period from WYs 2001 to 2009 were checked. For 

those computed for the Tinley Creek watershed, the 

PEST Tinley Creek watershed model performed best, 

followed by the PEST two-watershed model; for those 

computed for the Long Run watershed, the HSPEXP 

Long Run watershed model performed best, followed 

by the PEST Long Run watershed model and the PEST 

two-watershed model.

• Goodness-of-fit statistics in the calibration and two verification periods: For the calibration period, the PEST 

two-watershed and PEST Long Run watershed models 

performed best followed by HSPEXP Long Run 

model. The performance of all parameter sets declined 

in the two verification periods. For verification period 

one (WYs 1997 to 2000), the PEST two-watershed 

model performed better than the PEST Long Run 

model, followed by the HSPEXP Long Run model. For 

verification period two (WYs 2010 to 2011), the PEST 

two-watershed model and PEST Long Run model 

performed better than the other models. 

• The regional performance with the annual and overall 

S/R ratios and DMCs based on results from the entire 

period of WYs 1996 to 2011: Judging from the number 

of watersheds that have S/R ratios close to unity and 

the matches of observed and simulated DMC plots, the 

parameter sets obtained from the PEST two-watershed 

model and the PEST Long Run model perform better 

than other parameter sets in regional application.

Another criterion used to evaluate the regional performance of the parameter sets was whether a parameter set could 

break through the “watershed response group” boundaries 

discussed in the “Case for Recalibration” section. Parameter 

values obtained from the PEST two-watershed model had 

the best overall performance among the recalibrated parameter sets determined; therefore, this parameter set was used 

to compare with the North Branch parameter set (the best 

parameter set among the existing parameter sets), using the 

quantitative statistics for the study period. The results showed 

that the PEST two-watershed model parameter set resulted in 

a “very good” rating in PBIAS in five watersheds (across the 

watershed response groups) and the North Branch parameter 

set had a “very good” rating in three watersheds. Parameter 

sets obtained from the PEST two-watershed model calibration 

produced the best results in this study. 

For future studies, it is noted that the performance in NSE 

and RSR by the PEST two-watershed model is not as good as 

that by the North Branch parameter set. Possible causes may 

reside in the types of observation groups used to form the 

objective function or the assumption that parameters have the 

same values in the PEST two-watershed models. The candidate watersheds selected for use in the simultaneous calibration can also be an important factor for the performance of the 

resulting parameter values. These factors were not examined 

in this study. 
54 Lake Michigan Diversion Accounting Land Cover Change Estimation

References Cited

Barber, M.C., Baca, R.M., Bird, S.L., Doherty, J., Exum, L.R., 

Johnston, J.M., Lassiter, R.R., Rashleigh, B., Cyterski, M.J., 

Colarullo, S., Loux, N.T., Prieto, L.M., and Wright, C.J., 

2002, Regional assessment of fish health—A prototype 

methodology and case study for the Albemarle-Pamlico 

River Basin, North Carolina: U.S. Environmental Protection 

Agency, EPA/600/R-002/067, 178 p.

Bicknell, B.R., Imhoff, J.C., Kittle, J.L., Jr., Jobes, T.H., and 

Donigian, A.S., Jr., 2000, Hydrological simulation program, 

FORTRAN (HSPF) - User’s manual for release 12: Athens, 

Ga., U.S. Environmental Protection Agency Research Laboratory [variously paged].

Chaille, B., and Yen, B.C., 2000, Evaluating watershed 

changes using double-mass analysis - History in the 

Boneyard Creek stream gage record: University of Illinois 

at Urbana-Champaign, Department of Civil and Environmental Engineering, Civil Engineering Studies, Hydraulic 

Engineering Series No. 67, 48 p.

Chow, V.T., 1972, Handbook of applied hydrology: McGraw 

Hill, New York [variously paged].

Cocca, P., Doherty, J., and Kittle, J.L., Jr., 2003, Hydrologic 

calibration strategies for the HSPF watershed model - 

Identifying effective objective functions for use with the 

parameter estimation (PEST) program: World Water & 

Environmental Resources Congress 2003: p. 1–10.

Doherty, J., 2005, PEST, model-independent parameter 

estimation user manual (5th ed.): Watermark Numerical 

Computing [variously paged], accessed November 2013 at 

http://www.pesthomepage.org/Home.php.

Doherty, J., 2013, Addendum to the PEST manual: Watermark 

Numerical Computing, 293 p., accessed November 2013 at 

http://www.pesthomepage.org/Home.php.

Doherty, J., and Johnston, J.M., 2003, Methodologies for 

calibration and predictive analysis of a watershed model: 

Journal of American Water Resources Association, v. 39, 

p. 251–265.

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

Jr., 1984, Application guide for Hydrologic Simulation Program–FORTRAN (HSPF): Athens, Ga., U.S. Environmental 

Protection Agency-600/3-84-065, Environmental Research 

Laboratory, 177 p.

Donigian, A.S., 2002, Watershed model calibration and validation - The HSPF experience: Proceedings of the Water Environment Federation, National TMDL Science and Policy 

2002, Water Environment Federation, V 30. p. 44–73 

Duncker, J.J., and Melching, C.S., 1998, Regional rainfallrunoff relations for simulation of streamflow for watersheds 

in Du Page County, Illinois: U.S. Geological Survey WaterResources Investigations Report 98–4035, 80 p.

Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., 

Barnes, C., Herold, N., and Wickham, J., 2011, Completion 

of the 2006 National Land Cover Database for the conterminous United States: Photogrammetric Engineering and 

Remote Sensing, v. 77, no. 9, p. 858–864.

Gutierrez-Magness, A.L., 2005, A strategy for calibrating the 

HSPF model: Ph.D. Thesis, University of Maryland, College Park, 296 p.

Hey, D.L., Dreher, D.L., and Trybus, T.W., 1980, NIPC Chicago Waterways model - Verification/recalibration: Northeastern Illinois Planning Commission, Chicago, Ill.

Hummel, P., Kittle, J.L., Jr., and Gray, M., 2001, WDMUtil, 

version 2.0 - A tool for managing watershed modeling timeseries data, User’s manual: Decatur, Ga., AQUA TERRA 

Consultants, 157 p. 

Hydrocomp, 1977a, Des Plaines River hydrologic calibration: 

Chicago, Ill., Report to the Northeastern Illinois Planning 

Commission, Area wide Clean Water Planning, Water Quality Evaluation, v. V, no. 2.

Hydrocomp, 1977b, Hickory Creek hydrologic calibration: 

Chicago, Ill., Report to the Northeastern Illinois Planning 

Commission, Area wide Clean Water Planning, Water Quality Evaluation, v. VII, no. 2.

Hydrocomp, 1977c, Little Calumet River hydrologic calibration, Chicago, Ill., Report to the Northeastern Illinois Planning Commission, Area wide Clean Water Planning, Water 

Quality Evaluation, v. 8, no. 2.

Hydrocomp, 1977d, North Branch Chicago River hydrologic 

calibration: Chicago, Ill., Report to the Northeastern Illinois 

Planning Commission, Area wide Clean Water Planning, 

Water Quality Evaluation, v. VI, no. 2. 

Hydrocomp, 1979, Chicago sanitary and ship canal hydrologic 

calibration: Chicago Ill., Report to the Northeastern Illinois 

Planning Commission, Area wide Clean Water Planning, 

Water Quality Evaluation.

Kelliber, F.M., Leuning, R., and Schulze, E.D., 1993, Evaporation and canopy characteristics of coniferous forests and 

grasslands: Oecologia, v. 95, no. 2, p. 153–163.

Kim, S.M., Benham, B.L., Brannan, K.M., and Zeckoski, 

R.W., 2006, Automatic calibration of hydrologic parameters 

in HSPF using PEST: American Society of Agricultural 

and Biological Engineers paper number 062127, Portland, 

Oregon, 11 p.
References Cited 55

Iskra, I., and Droste, R., 2007, Application of non-linear 

automatic optimization techniques for calibration of HSPF: 

Water Environment Research, v. 79, no. 6, p. 647–659.

Lumb, A.M., McCammon, R.B., and Kittle, J.L., Jr., 1994, 

User’s manual for an expert system (HSPEXP) for calibration of the Hydrological Simulation Program-FORTRAN: 

U.S. Geological Survey Water-Resources Investigations 

Report 94–4168, 102 p.

Marce, R., Ruiz, C.E., and Armengol, J., 2008, Using spatially distributed parameters and multi-response objective 

functions to solve parameterization of complex applications 

of semi-distributed hydrological models: Water Resources 

Research, v. 44, W02436, 18 p.

Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., 

Harmel, R.D., and Veith, T.L., 2007, Model evaluation 

guidelines for systematic quantification of accuracy in 

watershed simulation: Transaction of American Society of 

Agricultural and Biological Engineers, v. 50, no. 3, 

p. 885– 900.

Natural Resources Conservation Service, 2007, Hydrologic 

soil groups, chap. 7 of Part 630 Hydrology, National 

Engineering Handbook: U.S. Department of Agriculture, 

accessed November, 2013 at http://directives.sc.egov.usda.

gov/OpenNonWebContent.aspx?content=17757.wba.

Peppler, Randy A., 1991, Installation and operation of a dense 

raingage network to improve precipitation measurements 

for Lake Michigan diversion accounting—Water year 1990: 

Illinois State Water Survey, 87 p.

Price, T.H., 1996, Application guide for hydrologic modeling 

in DuPage County using Hydrological Simulation Program 

– FORTRAN (HSPF)—Model organization and use, data 

collection and processing, calibration: DuPage County, Ill., 

Prepared for Stormwater Management Division, Environmental Concerns, 44 p. plus appendices.

Resource Coordination Policy Committee, 1998, Our community and flooding—A report of the status of floodwater 

management in the Chicago Metropolitan Area: 72 p.

RUST Environment and Infrastructure, 1993a, Diversion 

accounting update for the new 25-Gage Precipitation 

Network: Prepared for the U.S. Army Corps of Engineers, 

Chicago District, 5 p with six appendixes.

RUST Environment and Infrastructure, 1993b, Technical 

Memorandum on HSPF Parameter Assignments Used in the 

Lake Michigan Diversion Accounting Program, U.S. Army 

Corps of Engineers – Chicago District: Prepared for the 

U.S. Army Corps of Engineers, Chicago District, 7 p with 

one appendix.

Singh, V.P., editor, 1995, Computer models of watershed 

hydrology; Water Resources Publications, Colorado, USA, 

1130 p.

U.S. Army Corps of Engineers, Chicago District, 2004, Lake 

Michigan diversion—Findings of the fifth technical committee for review of diversion flow measurements and 

accounting procedures: Final Report, 181 p.

U.S. Army Corps of Engineers, Chicago District, 2009, Lake 

Michigan diversion - Findings of the sixth technical committee for review of diversion flow measurements and 

accounting procedures: Final Report, 198 p.

U.S. Army Corps of Engineers, Chicago District, 2014, Lake 

Michigan diversion accounting water year 2011 annual 

report: U.S. Army Corps of Engineers, Chicago District.

U.S. Army Corps of Engineers, Chicago District, 2014, Lake 

Michigan diversion - Findings of the seventh technical 

committee for review of diversion flow measurements and 

accounting procedures: Final Report, 152 p.

U.S. Department of Agriculture, Natural Resources Conservation Service, 1994, State soil geographic (STATSGO) data 

base - Data use information, miscellaneous publication 

number 1492 (rev. ed.): Fort Worth, Texas, National Soil 

Survey Center [variously paged].

U.S. Environmental Protection Agency, 2000, EPA BASINS 

Technical Note No. 6—Estimating hydrology and hydraulic 

parameters for HSPF: EPA–823–R00–012, 34 p.

Van Liew, M.W., Veith, T.L., Bosch, D.D., and Arnold, J.G., 

2007, Suitability of SWAT for the conservation effects 

assessment project - A comparison on USDA-ARS experimental watersheds: Journal of Hydrologic Engineering, v. 

12, no. 2, p. 173–189.

Wang, S., Fu, B.J., Gao, G.Y., Yao, X.L., and Zhou, J., 2012, 

Soil moisture and evapotranspiration of different land 

cover types in the Loess Plateau, China: Hydrology and 

Earth System Sciences, v. 16, p. 2883–2892, accessed 

May 30, 2013, at http://www.hydrol-earth-syst-sci.

net/16/2883/2012/.

Westcott, N.E., 2006, Continued operation of a 25-raingage 

network for collection, reduction, and analysis of precipitation data for Lake Michigan diversion accounting—Water 

year 2005: Illinois State Water Survey Contract Report 

2006–2, 67 p.

Westcott, N.E., 2013, Continued operation of a 25-raingage 

network for collection, reduction, and analysis of precipitation data for Lake Michigan diversion accounting - Water 

year 2012: Illinois State Water Survey Contract Report 

2006–2, 67 p.

Westenbroek, S.M., Doherty, J., Walker, J.F., Kelson, V.A., 

Hunt, R.J., and Cera, T.B., 2012, Approaches in highly 

parameterized inversion—TSPROC, a general time-series 

processor to assist in model calibration and result summarization: U.S. Geological Survey Techniques and Methods, 

book 7, chap. C7, 79 p., three appendixes.
Manuscript approved on April 1, 2015.

Edited by Stephanee Walker of the Rolla 

Publishing Service Center (PSC). Rolla, Missouri.

Final illustrations and tables by Suzanne Roberts of 

the Rolla PSC. Rolla, Missouri.

Layout and cover by Rosemary Stenback, USGS, 

Columbus PSC. Madison, Wisconsin.

Soong and Over—Analysis of Regional Rainfall-Runoff Parameters for the Lake Michigan Diversion Hydrological Modeling—Scientific Investigations Report 2015–5053

ISSN 2328-0328 (online)

http://dx.doi.org/10.3133/sir20155053