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doi:10.1016/j.je

�CorrespondE-mail addr

(B.M. Deal).1Present add

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Journal of Environmental Management 88 (2008) 1119–1130

www.elsevier.com/locate/jenvman

Assessing hydrological impact of potential land use changethrough hydrological and land use change modeling for

the Kishwaukee River basin (USA)

Woonsup Choia,�,1, Brian M. Dealb,2

aDepartment of Geography, University of Illinois at Urbana-Champaign, 607 S. Mathews Avenue, Urbana, IL 61801, USAbDepartment of Urban and Regional Planning, University of Illinois at Urbana-Champaign, 111 Temple Buell Hall, 611 E.

Lorado Taft Dr., Champaign, IL 61820, USA

Received 23 September 2006; received in revised form 26 May 2007; accepted 2 June 2007

Available online 8 August 2007

Abstract

We connected a cellular, dynamic, spatial urban growth model and a semi-distributed continuous hydrology model to quantitatively

predict streamflow in response to possible future urban growth at a basin scale. The main goal was to demonstrate the utility of the

approach for informing public planning policy and investment choices. The Hydrological Simulation Program—Fortran (HSPF) was set

up and calibrated for the Kishwaukee River basin in the Midwestern USA and was repeatedly run with various land use scenarios

generated either by the urban growth model (LEAMluc) or hypothetically. The results indicate that (1) the land use scenarios generated

by LEAMluc result in little changes in total runoff but some noticeable changes in surface flow; (2) the argument that low flows tend to

decrease with more urbanized areas in a basin was confirmed in this study but the selection of indicators for low flows can result in

misleading conclusions; (3) dynamic simulation modeling by connecting a distributed land use change model and a semi-distributed

hydrological model can be a good decision support tool demanding reasonable amount of efforts and capable of long-term scenario-

based assessments.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Urban growth modeling; Hydrological modeling; HSPF; Environmental impact assessment

1. Introduction

Streamflow plays an important role in establishing someof the critical interactions that occur between physical orecological processes and social or economic processes.Socio-economic processes including population dynamics,land use transformation, migration, transportation andagricultural practices closely interact with and greatlyaffect ecological processes, such as vegetative growth,ecological succession, habitat formation and maintenance(Voinov et al., 1999a). In both cases, hydrology and

e front matter r 2007 Elsevier Ltd. All rights reserved.

nvman.2007.06.001

ing author. Tel.: +1204 474 6337; fax: +1 204 474 7513.

esses: choi@cc.umanitoba.ca (W. Choi), deal@uiuc.edu

ress: Department of Civil Engineering, University of

nipeg, MB, Canada R3T 5V6.

333 5172.

hydrologic dynamics can work as a medium or canvas forunderstanding both the conditions for interactions to takeplace and the consequences that such interactions some-times elicit. One of the most important socio-economicprocesses for establishing far-reaching and long-termecological effects is land use transformation, especiallythe human-induced variety termed ‘urbanization.’ The far-reaching effects of urbanization can best be described by itsenormous impacts on basin hydrology and water quality(Ferguson, 1996; Bertrand-Krajewski et al., 2000; Valeoand Moin, 2000). Large proportional increases in imper-viousness in the form of roofs, sidewalks, roads, parkinglots, and turf grass can dramatically increase the speed andmagnitude of runoff (Dunne and Leopold, 1978; Chengand Wang, 2002). Understanding these complex socio-hydrologic dynamics is imperative for planning a moresustainable future.

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Fig. 1. Subbasins and reaches of the Kishwaukee River basin along with

weather and streamflow gauging stations. Metropolitan Rockford (left)

and Chicago (right) areas are shown along with the KRB in the inset map.

W. Choi, B.M. Deal / Journal of Environmental Management 88 (2008) 1119–11301120

The complexity and magnitude of each system (socio-economic and eco-hydrologic) require a systematic ap-proach for improving our understanding of each. In ouropinion, this is best accomplished through the adoptionand application of dynamic simulation modeling techni-ques. Explicit modeling of complex environmental pro-blems is essential for developing realistic descriptions ofpast behavior and the possible impacts of alternativemanagement policies (Costanza, 1991). Dynamic models ofcomplex and interconnected ecosystems enable scientists toexperiment with and come to understand the interactionsof dynamic system components (Forrester, 1969; Costanzaand Maxwell, 1991; Sklar and Costanza, 1991; Ruth andHannon, 1997). Modeling can provide assistance inmanaging uncertainty, developing feedbacks and lags,improving group decision-making techniques, and under-standing comprehensive learning tasks. The process ofmodeling can also help facilitate communication throughboth model results and model structure to find possibleemergent properties of a system.

Although a plethora of modeling and analysis has beendone in both hydrology and land use modeling, it is stillnovel to adopt (and connect) both hydrological models andland use change models for the purpose of informing publicplanning policy or investment choices. Good examples ofthis approach do exist and appear in several articles (e.g.,Beighley et al., 2003; Voinov et al., 1999a, b; Niehoff et al.,2002; Arthur-Hartranft et al., 2003). Voinov et al.(1999a, b) adopted a distributed cellular automata (CA)approach for both hydrological and land use changemodeling, where modeled land use change results weredynamically incorporated into the hydrological model.Niehoff et al. (2002) also utilized a distributed hydrologicalmodel with modeled land use change scenarios. Thesedistributed approaches have been described by others to becapable of a more accurate assessment of the hydrologiceffects of land use change, because their parameters includea physical interpretation of data and their model structuresgenerally allow for an improved representation of spatialvariability (Nandakumar and Mein, 1997). These models,however, are also very data-intensive and demand sub-stantial computing capability, which prevents them frombeing used for long-term assessments by planners.

On the other hand, Beighley et al. (2003) applied alumped-parameter hydrological model with a future landuse scenario from a CA-based dynamic urban growthmodel to a basin in southern California. In such anapproach, a basin is partitioned into subbasins, and theoutput from an urban growth model is incorporated intoeach subbasin to run a hydrological model. A similarapproach is adopted in this paper with a different researchdesign in a different geographical setting.

This paper presents the results of our work connecting aCA, dynamic, spatial urban growth model and a semi-distributed continuous hydrological model to quantita-tively predict hydrological variables in response to futureurban growth patterns in northeastern Illinois. More

specifically, this study focuses on how a local hydrologicalsystem responds to alternative land use scenarios generatedby a dynamic urban growth model and what substantivescientific, planning, and policy related implications emerge.Eventually, we intend to demonstrate the utility of theapproach as a decision support tool for regional planners.

2. The study region

The area of interest is the Kishwaukee River basin(KRB) in the Midwestern United States (Illinois andWisconsin); its drainage area is 3258 km2 (Fig. 1). Locatedroughly between the Metropolitan Chicago (ranked thethird in the US in population) and Rockford (ranked thethird in Illinois in population) areas, the KRB is underdevelopment pressure from both sides. Agriculture is thepredominant land use in the region. The 1992 NationalLand Cover Data (NLCD) from the US Geological Survey(USGS) indicates that row crops (mostly corns andsoybeans) cover more than 70% of the KRB. Urbanizedareas account for around 3% of the local land uses aroundthe year 2000.The annual mean precipitation and annual mean

temperature measured at Rockford, Illinois (NationalWeather Service Cooperative Station ID 117382) over theperiod 1971–2000 are 930mm and 8.9 1C, respectively.Monthly mean temperatures vary from below 0 1C inDecember, January and February to above 20 1C in thesummer months (June, July and August). About 10% of

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Table 1

Weather and streamflow gauging stations in and around the KRB

Station name Type (ID) Location Elevation

a.s.l.a (m)

Drainage

area (km2)

Rockford Greater Rockford Airport Weather (117382) 421120N/891060W 222.5 N/A

Kishwaukee River near Perryville, IL Streamflow (05440000) 4211104000N/8815905500W 211.0 2848.72

Kishwaukee River at Belvidere, IL Streamflow (05438500) 4211502200N/8815104700W 225.0 1394.55

South Branch Kishwaukee River near Fairdale, IL Streamflow (05439500) 4210603800N/8815400200W 223.7 1003.14

South Branch Kishwaukee River at DeKalb, IL Streamflow (05439000) 4115505200N/8814503400W 253.6 201.41

Piscasaw Creek near Walworth, WI Streamflow (05438283) 4213101800N/8813903900W 285.0 24.83

Source: USGS and National Climatic Data Center.aa.s.l.: above sea level.

Table 2

Land cover categories aggregated for HSPF and associated percent

pervious values

Land cover category in NLCD Aggregated

category for

HSPF

%

pervious

Open water W/W 100

Low-intensity residential LR 63

High-intensity residential HR/C/I/T 45

Commercial/industrial/transportation HR/C/I/T 45

Road Road 1

Bare rock/quarries/transitional Barren 50

Forest Forest 100

Shrubland G/P 100

Orchards/vineyards/other G/P 100

Grasslands/herbaceous G/P 100

Pasture/hay G/P 100

Row crops Ag 100

Small grains Ag 100

Urban/recreational grasses G/P 100

Woody wetlands W/W 100

Emergent herbaceous wetlands W/W 100

W. Choi, B.M. Deal / Journal of Environmental Management 88 (2008) 1119–1130 1121

the annual precipitation falls as snow (Allen Jr. andCowan, 1985).

There are five active streamflow gauging stations in theKRB, along with a weather station whose data were usedin this study (see Fig. 1). Table 1 briefly describes relevantstreamflow and weather stations information. Althoughadditional weather stations (besides Rockford) exist in thenorth and east of the KRB, they were considered toodistant to be useful for this analysis.

3. Semi-distributed hydrological modeling

3.1. Delineating subbasins with BASINS

For this work, we used three primary tools in ourhydrological modeling efforts: (1) the Better AssessmentScience Integrating Point and Nonpoint Sources, orBASINS (US Environmental Protection Agency, 2001);(2) land cover, elevation, and hydrography data from theUSGS; and (3) Hydrologic Simulation Program—Fortran,or HSPF (Bicknell et al., 2001).

BASINS was developed as a geographic informationsystem (GIS) based catchment assessment tool. It utilizesan ArcView GISs 3.x as a software platform to preprocessinput data for several hydrological models such as HSPFand Soil and Water Assessment Tool (SWAT). BASINS isuseful for delineating subbasins, reaches and outlets;processing land use and digital elevation datasets; andcalculating related subbasin parameters for the notedhydrological models.

The 1992 NLCD, National Elevation Dataset (NED)and National Hydrography Dataset (NHD) were obtainedfrom the USGS (http://seamless.usgs.gov). With NED andNHD, subbasins were automatically delineated in BASINSusing the default threshold BASINS suggested consideringthe basin size. Relevant physiographic parameters such asmean elevation, area, slope, etc. were subsequently calcu-lated and saved in an attribute table by BASINS. Fig. 1shows that twenty subbasins have been delineated withinthe KRB. The 20th subbasin was delineated by manuallyadding an outlet point at the location of a streamflowgauging station (USGS 05440000). The average subbasinsize is 157 km2, with the standard deviation of 127km2.

After delineating subbasins and calculating parameters,the BASINS data including the basin model and land coverdata were exported to HSPF to calculate streamflow withinthe KRB. For simplicity, the USGS NLCD land covercategories were aggregated as in Table 2. Percent perviousvalues were estimated for each aggregated land usecategory, based on previous works in the literature (Brunand Band, 2000; Choi and Ball, 2002).

3.2. HSPF modeling

HSPF is a semi-distributed hydrological model em-bedded in BASINS. A previous work has shown it to beuseful for analyzing long-term hydrological effects, espe-cially in largely urbanized areas (Borah and Bera, 2003).HSPF has been developed to be readily applicable to mostbasins in the United States using readily-available weather,hydrologic, topographic, and land use information—generally supplied with BASINS program (Rahman andSalbe, 1995). It has three modules, PERLND, IMPLND,and RCHRES, which represent pervious land segments,

ARTICLE IN PRESSW. Choi, B.M. Deal / Journal of Environmental Management 88 (2008) 1119–11301122

impervious land segments and reaches/reservoirs, respec-tively. A river basin is divided into subbasins which haveboth pervious land segments and impervious land segmentsin each of them. Hydrological and water quality processesare simulated by both PERLND and IMPLND modules ineach subbasin, and routing to downstream subbasins issimulated by RCHRES module.

HSPF requires hourly meteorological data sets and awatershed data set. In BASINS, hourly meteorologicaldata are stored in a WDM file for each state (http://www.epa.gov/waterscience/ftp/basins/wdm_data/).‘IL.WDM’ file was used in this study, which contains themeteorological data set measured at Rockford, Illinois, aspreviously noted. A watershed data set as a WSD file iscreated by exporting the subbasin boundaries and para-meters in BASINS for HSPF. A single WSD file containsall the subbasins information in it as text. An HSPF projectis created by combining the WDM file and the WSD file,and saved as ‘*.UCI’ in a text format. A UCI file contains aset of all the parameters and paths to other files.

4. Model calibration

The HSPF model was calibrated against the observedstreamflow data from the USGS gauging station 05440000Kishwaukee River near Perryville, IL (Table 1). Then thecalibrated model was applied to a small subbasin, forwhich observed streamflow data are available, to show thatthe calibrated model parameters properly work in differentlocations in the KRB. This is the so-called ‘modelconfirmation’ conducted by Wicklein and Schiffer (2002),and Cao et al. (2006) adopted a similar approach withSWAT model. For model confirmation, a subbasindraining at the gauging station USGS 05439000 SouthBranch Kishwaukee River at DeKalb, IL (Table 1) wasselected. A separate HSPF model (UCI file) was built forthe subbasin, called the South Branch Kishwaukee RiverBasin (SBKRB) hereafter. The SBKRB has the area of201.41 km2 and located in the southeastern part of theKRB.

In our analysis, the years 1988–1989 were used as thecalibration period and 1993–1994 as the validation period.The periods were selected due to the hydrometeorologicaldiversity that they exhibit. The KRB was very dry in 1988,and the Great Flood in the Mississippi River Basinoccurred in 1993. We expected the model to be bettercalibrated with those calibration and validation periodsthat have contrast conditions. The periods are also relevantfor matching the known land cover at the time (recall the1992 NLCD is used here).

Brun and Band (2000) showed that HSPF works well forthe same basin with different degrees of development. Theycalibrated HSPF for three different years with differentmeteorological conditions and land cover conditions byadjusting only hydrology-related parameters. A single setof parameters calibrated for these meteorological condi-tions also worked for different land cover conditions.

Therefore, calibrating for different degrees of developmentfor the KRB is not necessary if a single set of parameters isfound to work for different climates.When it comes to calibration criteria, Nash-Sutcliffe

coefficient of efficiency (E) and the deviation of runoffvolumes (Dv) are adopted in this study as recommendedand explained by ASCE (1993). E is used to measure‘goodness-of-fit,’ while Dv is for assessing the relativedeviation of the mean value of the simulated data from thatof the observed data.The equation for E is as follows:

E ¼ 1:0�

PNi¼1ðOi � PiÞ

2

PNi¼1ðOi � OÞ2

,

where Pi is simulated streamflow at time i, Oi is observedstreamflow at time i, and O is the mean of observedstreamflow for the whole period. E is 1.0 for a perfectmatch, and if E ¼ 0.7, it means that the ratio of meansquare error to the variance in the observed data is 0.3(Legates and McCabe Jr., 1999). E value of zero meansthat the model’s prediction is no better than using the meanof observed values.The equation for Dv is as follows:

Dvð%Þ ¼ ðP�OÞ=O� 100,

where P and O are simulated and observed total runoffvolumes respectively for the period of interest. Dv can beany value, but smaller absolute values are desired. Positivevalues mean overestimation and negative values under-estimation.Legates and McCabe Jr. (1999) strongly recommend that

at least one absolute measure (e.g., root mean square erroror mean absolute error) and observed and modeled meansand deviations be reported, as well as goodness-of-fitmeasures. Therefore, this study will show mean and Dv atannual scales, and mean, mean absolute error (MAE) andE at daily scales. At the daily scale, Dv values for the Q5flow (daily flow exceeded by 5% of the total records) andQ95 flow (daily flow exceeded by 95% of the total records)will be shown to see the model performance on very highand low flow events. Q95 flow is commonly used as anindex of low flow conditions (Boorman and Sefton, 1997),and there are some studies that examined the changes in Q5and Q95 under climate change (e.g., Gellens and Roulin,1998; Drogue et al., 2004).Tables 3 and 4 show the calibration summary at annual

and daily scales, respectively. At the annual scale, bothKRB and SBKRB models satisfactorily simulate annualwater budget within 10% error except during the calibra-tion period of the SBKRB model (12.4%). The dailyE values are over 0.8 during both calibration andvalidation periods. This is higher than other HSPFapplications. For example, Johnson et al. (2003) obtainedE ¼ 0.65 for daily streamflow, and Brun and Band (2000)obtained R2

¼ 0.69 for weekly streamflow. The Dv valueswith Q5 and Q95 flows are not as satisfactory as those with

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annual runoff, therefore caution must be used wheninterpreting the results regarding Q5 and Q95 flows.

Fig. 2 presents the scatterplots of observed (Obs) andsimulated (Sim) daily runoff in millimeters for thecalibration (1988–1989) and the validation (1993–1994)periods. As can be seen, the model fairly well reproducesobserved daily runoff during both calibration and valida-tion periods, even though it noticeably underestimatessome very high flow events during the validation period.

Final values of major parameters in PERLND moduleare presented in Table 5. Annual water balance is sensitiveto LZSN, UZSN, DEEPFR, LZETP, and INFILT, andthe shape of daily hydrograph and extreme flows aresensitive to AGWRC, INTFW, and IRC. Kang (2004)

Table 3

Calibration summary at an annual scale

Model Statistics Calibration

period

Validation

period

KRB Mean observed

runoff (mm)

183.36 406.42

Mean simulated

runoff (mm)

198.45 386.09

Dv (%) 8.23 –5.00

SBKRB Mean observed

runoff (mm)

189.81 440.27

Mean simulated

runoff (mm)

213.35 406.04

Dv (%) 12.40 –7.78

Table 4

Calibration summary at a daily scale for the KRB model

Statistics Calibration

period

Validation

period

E for daily runoff series 0.82 0.80

Mean observed daily runoff (mm) 0.50 1.12

Mean modeled daily runoff (mm) 0.54 1.06

MAE (mm) 0.16 0.39

Dv for Q5 flow (%) –1.07 –21.68

Dv for Q95 flow (%) –21.56 33.89

Fig. 2. Scatterplots of observed (Obs) and simulated (Sim) daily runoff (in m

performed the study in a basin in northeastern Illinois,which is east of the KRB, and provides parameter values insimilar orders.

5. Model simulation

5.1. Control simulation

The model running period is 1980–1995, and the analysisperiod is 1988–1995 (8 years). The analysis period waschosen as such since the 1992 NLCD was used. The yearsprior to 1988 are a warming-up period so that the systemreaches an equilibrium state. The control simulation wasconducted with the existing (Control) land use data andcalibrated parameters. Based on the Control simulation,the 8-year hydrology of the KRB was established, whichrepresents the current runoff regime and will be comparedto the results under future land use scenarios. The main

m) during calibration (1988–1989) and validation (1993–1994) periods.

Table 5

Calibrated PWATER parameters in PERLND module compared with

those from other studies

Parameter This

study

In et al. (2003) Al-Abed

and

Whiteley

(2002)

Kang

(2004)

LZSN (mm) 203.2 109.22–147.32 33–74 152.4

UZSN (mm) 20.3 1.19–1.91 12.76–30.9 28.65

INFILT

(mm/h)

2.79 8.89–25.4 0.69–8.37 4.06

DEEPFR

(unitless)

0.15 0.05–0.45 N/Ra 0.3

AGWRC

(unitless)

0.975 0.88–0.91 N/R 0.98

INTFW

(unitless)

1.7 1–1.7 N/R 0.75

IRC

(unitless)

0.7 N/R N/R 0.5

LZETP

(unitless)

0.2 0.2–0.7 N/R 0.1

The units were converted to metric.aN/R: not revealed.

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output of the model includes streamflow (in cubic feet persecond), surface flow (in inch), interflow (in inch) and baseflow (in inch) from each subbasin at various time scales.The values from Subbasin 13 were used for the analysis,which is the most downstream subbasin of the KRB(Fig. 1), after unit conversion from imperial to metric.

5.2. Simulations with future land use scenarios

Future land use scenarios were created from the Landuse Evolution and impact Assessment Model (LEAM;http://www.leam.uiuc.edu). LEAM was developed tosimulate urban growth at a high spatial resolution(30m� 30m). It was designed to project the generalpatterns of urban growth for several decades into thefuture based on investments and policies. Completedescription about LEAM is provided by Deal (2001) andDeal (2003), and brief information is given below as inWang et al. (2005).

LEAM utilizes a cellular automata (CA) approach tosimulate land use transformations across landscape thatare attributed to spatial and dynamic interactions amongeconomic, social, and ecological systems in the region. Itstarted as a model for simulating land use change, and hasevolved into a system of related models. The overallconcept of LEAM is presented in Fig. 3.

LEAM mainly consists of model drivers and impactmodels. The model drivers compose LEAMluc, whichrefers to the part of LEAM that simulates the actualchanges in land use over the landscape. Drivers refer tothose forces that contribute to urban land use change andthe forces are typically human activities. Model drivers arerun simultaneously in each grid cell, and the outputs fromthe model drivers are used to calculate the probability of acell changing to an alternative land use (low-densityresidential, commercial or open space). The probabilitydepends on both the local attributes of the cell (slope,

Fig. 3. Overall concept of LEAM (adapted from Deal, 2001, p. 385).

distance to the closest ramp, road, etc.) and the state ofneighboring cells in the last time step.The products of LEAMluc model runs are sequential

land use maps under different economic growth and policyscenarios. These outputs in the form of land use maps arethen used as inputs to impact models that analyze long-term environmental and fiscal impacts. Based on theimpact models, sustainability indices are calculated to feedback into the model drivers.In this study, simulated land use maps under ‘Uber,’

‘High,’ and ‘Base’ economic growth scenarios wereprovided by LEAMluc. The population growth wasassumed to be the only driver which alters regionaleconomy and hence land use through time, all else beingequal. The Base scenario is that county populations wouldchange as projected by the US Census Bureau. A 125%and 150% weight is given to the population projection togenerate the High and the Uber scenarios, respectively.LEAMluc simulation was conducted for a 57-year

period (1995–2051). In other words, the first simulationyear’s result is assumed to represent the land use conditionof the year 1995 and the final year’s result that of the year2051. Land use information for each simulation year wasutilized to create an HSPF project (UCI file) for eachsimulation year. In other words, the HSPF model was runrepeatedly 57 times for 1980–1995 with different land usedata each time.The LEAMluc-simulated changes in land use types are

shown in Fig. 4 as of the final year under differentscenarios (Base, High and Uber, respectively) along withthe current condition (Control). It can be noted that LR(see Table 2 for acronyms) and HR/C/I/I are projected toincrease noticeably while Forest, G/P and Ag somewhatdecrease. The percentage of urban land uses (LR, HR/C/I/Iand Road) is projected to increase from 2.9% (Control)to 6.0% (Uber) by 2051. The land use change patternsare very linear throughout the simulation period in all landuse types.

6. Results and discussion

6.1. Changes in runoff regime of the KRB

Table 6 summarizes the potential changes in runoffregime of the KRB at the outlet (Subbasin 13) underdifferent land use scenarios. As seen in the table, meanannual runoff is predicted to hardly change. Even underUber scenario, mean annual runoff has been predicted toincrease by only 1.7% by 2051, which may not beconsidered significant considering the model error. Whiteand Greer (2006) even found that total runoff changedinsignificantly in spite of urbanization from 9% to 37% ofa basin in San Diego County, California during the periodof 1966–1999. Instead, only surface flow shows meaningfulincreases in this study, especially under Uber scenario (upby 38.5%). The relative portions of runoff components arepredicted to change by only a few percent even in Uber

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Fig. 4. LEAMluc-projected land use changes under different scenarios by 2051 (see Table 2 for acronyms).

Table 6

HSPF predicted changes in runoff components (percent changes with

respect to Control in parenthesis)

Scenario variables Control Base

2051

High

2051

Uber

2051

Mean runoff (mm/

yr)

289.9 291.3 291.5 294.8

(0.5) (0.6) (1.7)

Mean surface flow

(mm/yr)

19.8 21.5 22.2 27.4

(8.6) (11.9) (38.5)

Mean interflow

(mm/yr)

52.1 52.1 52.1 51.7

(0.0) (0.0) (–0.7)

Mean base flow

(mm/yr)

218.0 217.6 217.2 215.7

(–0.2) (–0.4) (–1.1)

Q5 flow (m3/s) 78.40 78.44 78.44 79.00

(0.0) (0.0) (0.8)

Q95 flow (m3/s) 4.23 4.29 4.37 4.65

(1.4) (3.2) (9.9)

Number of days

with mean flow

below 1.95m3/s

15 13 13 10

(–13.3) (–13.3) (–33.3)

Number of days

with mean flow

over 152.77m3/s

36 36 36 39

(0.0) (0.0) (8.3)

W. Choi, B.M. Deal / Journal of Environmental Management 88 (2008) 1119–1130 1125

scenario. Base flow is 75% and surface flow is 7% of thetotal runoff in Control, and they change to 73% and 9% inUber scenario, respectively. Such absolute and relativeincreases in surface runoff can result in a different capacityof water to transport material and to erode banks andbeds. Using historical land use data, Kim et al. (2005)performed similar analyses for a basin in central Korea andfound that direct runoff increased and base flow decreasedboth relatively and absolutely with urbanization, which isin agreement with this study. They also found that eventhough urban area increased by only 5.5% between 1986and 2002 the contribution of urban area to total directrunoff increased by 14.3%.

Q5 flow, Q95 flow and the number of days with meanflow below 1.95m3/s or over 152.77m3/s are also predicted

to change much more under Uber scenario than under Baseor High scenario. The number of days with mean flowbelow 1.95 or over 152.77m3/s is an indicator of thefrequency of extremely low or high flow events for theKRB. Annual peak flow is an instantaneous event andprovides no information on flows of previous or followingdays. Therefore, the highest monthly mean flow is betterindicative of high flow conditions since it represents thestreamflow condition of an exceptional month instead ofan exceptional single day. So is the lowest monthly meanflow of low flow conditions. The lowest monthly meanstreamflow was recorded in January 1959 as 1.95m3/s andthe highest monthly mean streamflow in October 1979 as152.77m3/s at the gauging station 05440000 during theperiod 1941–2000, respectively.It is interesting to note that under Uber scenario, the

number of days with low flows is predicted to change withmuch larger percentage than that with high flows (–33.3%vs. 8.3%) and Q95 flow is predicted to increase by 9.9%while Q5 flow increase by only 0.8%. Similar results wereobtained under Base and High scenarios. Overall, lowflows seem to be more sensitive than high flows in thissystem, even though the calibration results with Q5 andQ95 flows are not satisfactory. This may be in part due tothe model characteristic. HSPF has been built suitable forlong-term hydrologic simulation, not for simulating intensesingle event storm (Borah and Bera, 2003). High flows mayshow changes at an hourly scale but may not change muchat a daily scale, especially in semi-distributed conceptualmodels like HSPF. Middelkoop et al. (2001) also point outthat the reliability of the HSPF simulation results is higherfor low flow conditions due to its lower temporalvariability. Such a marginal increase in high flow was alsofound in In et al. (2003) and Verbunt et al. (2005). In et al.(2003) find that peak flows would increase with much lesspercentage than that of runoff volume in most scenarios,and sharp increases in runoff volume and peak flow inan upstream subbasin would diminish at the outlet.

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Fig. 5. HSPF projected annual total runoff (upper panel) and annual total

surface flow (SURO; lower panel) with sequential land use change

projected by LEAMluc under different growth scenarios.

W. Choi, B.M. Deal / Journal of Environmental Management 88 (2008) 1119–11301126

Verbunt et al. (2005) and Moglen and Beighley (2002) alsopointed out the importance of spatial variability of theimpact on peak discharges within a basin. Beighley et al.(2003) report that annual runoff depths increase with largerpercentage than annual maximum discharges with urbani-zation, implying that small peaks are more sensitive tourbanization than large peaks.

However, the direction of changes with low flow issomewhat surprising, since low flow tends to decrease withmore imperviousness mainly due to reduced groundwaterrecharge, as found by Rose and Peters (2001). On the otherhand, Douglas et al. (2000) find significant upward trendsin low flows in Ohio, the north central, and the upperMidwest regions in the US, but this is partially due toincreases in annual precipitation and more storage of waterin basins. The works by Rose and Peters (2001) andDouglas et al. (2000) are based on empirical data analysis,while this study adopts a dynamic modeling method. Therehas been little research that addressed the impacts of landuse change on low flows with modeling approaches, butmore modeling experiments provide a clue to this problemin the following section.

6.2. Streamflow response to sequential changes in land use

The potential changes in runoff were also examinedtemporally. Fig. 5 shows the simulated mean annual runoff(upper panel) and surface flow (SURO, lower panel) indifferent simulation years. All the curves are close to linear,and the slope of Uber scenario is much steeper that those ofHigh and Base scenarios. Similar patterns appear in meanannual interflow and base flow (both not shown), exceptthat interflow and base flow show linearly decreasingtrends. These results are in agreement with those fromChangnon et al. (1996), Calhoun et al. (2003) and Bhaduriet al. (2001), but not with that from Brun and Band (2000).It can be attributed to the minute increase in imperviousland segments in this region. Imperviousness was increasedup to 90% in Brun and Band (2000) to find a logisticrelationship between runoff ratio and imperviousness andan exponential relationship between base flow andimperviousness. On the other hand, Wissmar et al. (2004)find that the magnitudes of flood flows with certainrecurrence intervals (e.g., 10 years) tend to increaseabruptly around 10–23% and 46–74% impervious levels.In the present study, the percentage of urban land uses isstill too low to result in nonlinear changes in flows.

It should be noted that this sequential analysis is notfrom observed land use change in the real world, but frommodeling experiments. Readily available historical land useor land cover data sets in digital formats tend to have longtemporal gaps from one another. Therefore, it is very time-consuming to obtain enough number of historical data setsfor such sequential analysis. De Roo et al. (2003), Brun andBand (2000) and Kim et al. (2005) used historical datasets of only three different years, and Mattikalli et al.(1996) 6 years.

The regional land use changes for the KRB as projectedby the LEAMluc simulation were minimal and did notresult in significant hydrological impacts. To create notableimpacts, arbitrary land use changes were made todrastically increase imperviousness solely for an experi-mental purpose. This experiment is similar to the onecarried out by Brun and Band (2000), but was conductedsomewhat differently in this study. Ag was converted toimpervious LR gradually so that imperviousness increasesup to 89%. At each selected imperviousness level, the KRBmodel was run and monthly mean flow was calculated overthe period of 1988–1995.Fig. 6 shows the potential changes in the distribution of

monthly mean streamflow values at four selected imper-viousness levels. It reveals that not only the mean ofmonthly mean flows, but also their variance would increase

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Fig. 6. HSPF predicted distribution of monthly mean streamflow values (in cubic meter per second) with increasing imperviousness.

Fig. 7. HSPF predicted Q5 and Q95 flows (in m3/s) with different percent

impervious values in Subbasin 18.

W. Choi, B.M. Deal / Journal of Environmental Management 88 (2008) 1119–1130 1127

with increasing imperviousness, and the distribution ofmonthly mean flow values get closer to normal distribu-tion. However, deriving a regression model as was done byBrun and Band (2000) does not make much sense in thiscase, since there is a wide range in monthly mean flowvalues at each imperviousness level. It is technicallypossible to derive a regression model with percentimpervious values as an independent variable and monthlymean flow values as a dependent variable, but theregression model is not meaningful due to a very low R2

value. In that context, the R2 values (0.67 and 0.54)obtained by Brun and Band (2000) look excessively high.

In the same way, Subbasin 18 where the City of DeKalbis located (this is one of the most developed subbasins inthe KRB, and is located upstream) was selected for anadditional analysis. The trends of Q5 and Q95 flows withcontinuing increases in percent impervious are plotted on a

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graph in Fig. 7. Q5 flow is predicted to linearly increasewith increasing percent impervious, while Q95 is predictedto increase initially (up to about 15–17% impervious) anddecrease thereafter linearly. It makes sense that Q5 flowkeeps increasing with increasing imperviousness, and it canbe inferred from the trend shown in Fig. 6. The temporalpattern of Q95 is surprising due to its initial increase. Thisinitial increase in Q95 flow is consistent with the changes inQ95 flow and the number of days with mean flow below1.95m3/s shown in Table 6. The up-and-down pattern ofQ95 flow could be understood by scrutinizing the rank ofdaily flow values at each imperviousness level and the dailyhydrograph in any short time periods. At relatively lowerimperviousness levels (e.g., 15% or less), only veryextremely low flow events (e.g., Q99 flow) are predictedto decrease and the rest increase or barely change. At thenext higher imperviousness level, the leverage point movesforward, i.e., Q98 flow decreases and larger flows increase,and so on. That is why Q95 flow is predicted to slightlyincrease initially (Table 6 and Fig. 7). This might bepartially due to a model characteristic, but the trend withlow flows is consistent with the literature.

6.3. Dynamic simulation modeling as a decision support tool

A new development plan in a region, for example,building a new highway, or relocating a large factory, canresult in a widespread change in landscape, and such achanging landscape will incur various fiscal, environmen-tal, and societal impacts. Such a change in landscapeand its impacts need to be assessed before the plan isimplemented, and as pointed out in Section 1, dynamicmodeling approach can provide plausible projectionson those impacts and help facilitate communicationamong stakeholders and policy-makers. Spatially explicitmodeling is especially helpful for such planning problemsbecause they often require spatial reference (Hormannet al., 2005).

This study exemplifies a method for scenario-basedimpact assessments. The impact of future land use changeson regional hydrology deserves great attention, andsimulation modeling is found to be a useful tool in thisstudy with reasonable amount of effort. Once the model isset up and calibrated, it can perform various analyses onhydrology, and some of the results are presented in thispaper. There are a few studies that adopted similarapproaches. For example, Bellot et al. (2001) investigatedhow afforestation would affect the runoff and annualaquifer recharge for the next 20 years in eastern Spain, andHe (2003) developed an integrated model to analyze theeffect of land use change on hydrology and non-pointsource pollution. The methodological advantage of thisstudy is that the land use scenarios generated by a land usechange model were sequentially incorporated into ahydrological model. It enables one to see what happensin between, not only to compare the present and futureconditions. In addition, this study does not require as much

time and computing capacity as fully distributed ap-proaches do.

7. Summary and conclusion

This paper has attempted to connect a semi-distributedhydrology model (HSPF) and a dynamic urban growthmodel (LEAMluc) as a method for examining the implica-tions of socio-economic dynamics on the hydrology of theKishwaukee River basin (KRB) in the Midwestern USA. Asemi-distributed HSPF model was successfully calibratedand repeatedly run with various land use scenariosgenerated by LEAMluc and hypothetical scenarios.The findings of this study can be summarized in three

ways. First, the land use scenarios generated by LEAMlucresult in little changes in total runoff but some noticeablechanges in surface flow under Uber scenario which isassociated with very high population growth. This impliesthat the region is not likely to undergo significant changesin flood frequency or the availability of surface waterresource due to future urban growth pressures, althoughthere may be some changes in export of sediments ornutrients from land to streams. Secondly, the argumentthat low flows tend to decrease with more urbanized areasin a basin was confirmed in this study, but the selection ofindicators for low flows can result in misleading conclu-sions. Therefore, the persistence of low flows need to beconsidered rather than just threshold values when assessingthe impact on low flows. Finally, this study shows thatdynamic simulation modeling by connecting a distributedland use change model and a semi-distributed hydrologicalmodel can be a good decision support tool demandingreasonable amount of efforts and capable of long-termscenario-based assessments.

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