Journal of Hydrology 485 (2013) 113–125

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Journal of Hydrology

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Hydrological response to urbanization at different spatio-temporal scalessimulated by coupling of CLUE-S and the SWAT model in the Yangtze River Deltaregion

Feng Zhou a, Youpeng Xu a,⇑, Ying Chen b, C.-Y. Xu c, Yuqin Gao a, Jinkang Du a

a School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, Chinab College of Geographic Sciences, Fujian Normal University, Fuzhou 350007, Chinac Department of Geosciences, University of Oslo, Norway

a r t i c l e i n f o

Article history:Available online 8 January 2013

Keywords:UrbanizationHydrological responseSpatio-temporal scaleSWAT model

0022-1694/$ - see front matter � 2013 Elsevier B.V. Ahttp://dx.doi.org/10.1016/j.jhydrol.2012.12.040

⇑ Corresponding author. Tel.: +86 2583595069.E-mail address: xuyp305@yahoo.com.cn (Y. Xu).

s u m m a r y

The Main objective of the study is to understand and quantify the hydrological responses of land use andland cover changes. The Yangtze River Delta is one of the most developed regions in China with the rapiddevelopment of urbanization which serves as an excellent case study site for understanding the hydro-logical response to urbanization and land use change. The Xitiaoxi River basin, one of the main upstreamrivers to the Taihu Lake in the Yangtze River Delta, was selected to perform the study. The urban area inthe basin increased from 37.8 km2 in 1985 to 105 km2 in 2008. SWAT model, which makes direct use ofland cover and land use data in simulating streamflow, provides as a useful tool for performing such stud-ies and is therefore used in this study. The results showed that (1) the expansion of urban areas had aslight influence on the simulated annual streamflow and evapotranspiration (ET) as far as the wholecatchment is concerned; (2) surface runoff and baseflow were found more sensitive to urbanization,which had increased by 11.3% and declined by 11.2%, respectively; (3) changes in streamflow, evapo-transpiration and surface runoff were more pronounced during the wet season (from May to August),while baseflow and lateral flow had a slight seasonal variation; (4) the model simulated peak dischargeincreased 1.6–4.3% and flood volume increased 0.7–2.3% for the selected storm rainfall events at theentire basin level, and the change rate was larger for smaller flood events than for larger events; (5) spa-tially, changes of hydrological fluxes were more remarkable in the suburban basin which had a relativelarger increase in urbanization than in rural sub-basins; and (6) analysis of future scenarios showed theimpacts of urbanization on hydrological fluxes would be more obvious with growth in impervious areasfrom 15% to 30%. In conclusion, the urbanization would have a slight impact on annual water yield, but aremarkable impact was found on surface runoff, peak discharge and flood volume especially in suburbanbasins in the study area. The study suggested that more attention must be paid for flood mitigation andwater resources management in planning future urban development in the region.

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1. Introduction

Urbanization, which typically replaces a permeable vegetatedland surface with impervious surface areas, significantly changesthe hydrologic fluxes of a drainage basin. It causes local decreasesin infiltration, canopy interception and the water holding ability ofthe basin (Rose and Peters, 2001; Aronica and Cannarozzo, 2000),and has the potential to produce huge floods (Huang et al., 2008;Olang and Furst, 2010).

Yangtze River Delta is one of the most developed regions inChina; it only covers 1% of Chinese territory and 6% of Chinese pop-ulation, brings about 17% of the Gross Domestic Product (GDP) in

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China (Dong, 2004). With the dramatic economic growth, densestpopulation and high degrees of urbanizations, the region is facingserious problems of flood risk, water quality deterioration andwater shortage etc., which seriously threatens the living environ-ment of local population and restricts the sustainable developmentof regional economy. To promote effective ways to rehabilitate theregional environment and develop programs of sustainable land-resources utilization, it is significant and imperative to understandthe potential consequences of urbanization on hydrologic fluxes inthis region (Xu et al., 2010; Chen et al., 2009).

There have been many studies examining the hydrological re-sponse to urbanization around the world and most results indi-cated the impact of urbanization on water resources is obviousbut with varying characteristics in different regions. Brun and Band(2000) assessed the effects of urbanization on watershed behavior

114 F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125

in upper Gwynns Falls from pre-urbanized times to 1990 andfound that baseflow had declined by 20%; however, Brandes et al.(2005) suggested that increases in impervious area might not re-sult in measurable reductions in baseflow at the watershed scaleafter examining long-term streamflow records from unregulatedwatersheds of the lower to middle Delaware River basin. Beighleyet al. (2003) also found urbanization was shown to increase peakdischarges and runoff volume while decrease streamflow variabil-ity in a Mediterranean climate. Jennings and Jarnagin (2002)showed a significant increase in streamflow response to land useand land cover change (LUCC) following a growth in imperviousareas from 3% to 33% in Virginia, USA. Similar findings were re-ported by Kim et al. (2002) and Beighley et al. (2003).

However, Brun and Band (2000) did not detect any significantincrease in annual runoff coefficients in an urbanizing watershedof the Baltimore Metropolitan area of the USA with 20% increasesin urban development between 1970 and 1987. Chang (2003) alsoshowed no significant increase (less than 2%) in annual runoffwhen land use was converted from agricultural land to low-densitysuburban developments in a simulation study of a southeasternPennsylvania watershed. Similarly, Chang (2007) sought to com-pare changes in the streamflow characteristics of urbanized catch-ments and less-urbanized catchments using various streamflowindices with different temporal scales, and found the influence ofurbanization on the hydrology of a region was more pronouncedat a shorter time-scale, which suggested that annual and monthlyscale analysis may not be appropriate for detecting the urban sig-nal on hydrology.

Meanwhile, the spatial pattern of urbanization was suggested toaffect hydrological fluxes. Petchprayoon et al. (2010) found that in-crease in streamflow at downstream areas of the rapid urbaniza-tion was significantly greater than that at upstream areas in theYom watershed in central–northern Thailand, and the similar find-ings were reported by Old et al. (2003) and Guo et al. (2008). Inaddition, Niehoff et al. (2002) showed that the influence of landuse conditions on storm runoff generation depends greatly onthe rainfall event characteristics and on the related spatial scalein a meso-scale catchment in SW-Germany, i.e., the influence isonly relevant for convective storm events with high precipitationintensities in contrast to long-lasting advective storm events withlow precipitation intensities.

These previous research findings suggest that impact of urban-ization on hydrology depends on the spatial and temporal scales,climate variability, and physical characteristics of the study region.Hence, apparently, it is necessary to do more systematical investi-gations on the problem. As stated by Wagener (2007), the hydro-logical impacts of land use and land cover changes are stillcontentious issues and further research is necessary. A case studyin the subtropics monsoon climate environment and rapid urban-ization of the Yangtze River Delta, might improve the understand-ing of the past urbanization impact on hydrology processes.

Hydrological models are frequently used for quantifying the im-pact of land use change on hydrological components on watershedscale (Liu et al., 2006; Jiang et al., 2012; Ren et al., 2012; Yang et al.,2012). The principle of model selection is based on the purpose ofthe study and data accessibility, and many previous studies havedemonstrated the ability of SWAT (Soil and Water AssessmentTool) in detecting the impact of land use and/or climate changeson hydrological variations in different catchments in many coun-tries of the world (e.g., Jayakrishnan et al., 2005; Heuvelmanset al., 2005; Wu and Johnston, 2007; Ma et al., 2009; Li et al.,2009; Du et al., 2012).

In this study the SWAT model is used to examine the impacts ofland use change on hydrological fluxes in a rapid urbanizationregion in the lower reach of the Yangtze River. The specific objec-tives of this study are to: (1) identify the historical changes of

urbanization over a period of 23 years (1985–2008); (2) under-stand and quantify the different characteristics of urbanization im-pacts on hydrological fluxes at various spatial and temporal scales;and (3) evaluate the potential hydrological response to urbaniza-tion scenarios in the near future to provide a reference for urbansustainable development in the study area.

2. Materials and method

2.1. Study area

Xitiaoxi basin, located in the northwest of Yangtze River Deltabetween latitude of 30�230–31�110 and longitude of 119�140E–120�290E, is selected to perform the study. It covers an area of1371 km2 at the Hengtangcun station (Fig. 1). The study area ischaracterized by a subtropical climate, with an average annualtemperature and precipitation of 15.5 �C and 1465.8 mm, respec-tively. More than 75% of the annual precipitation falls in wet sea-son (from April to October), while less than 25% occurs in the dryseason (from December to March). The mean annual potentialevaporation varies from 800 mm to 900 mm, with the maximumevaporation occurs in July and August. The detailed informationon the physical characteristics of the basin (i.e. land cover and ma-jor soil types) is discussed in Section 2.4.2.

As one of the most important tributaries in the upstream of Tai-hu Lake basin in the Yangtze River Delta, the Xitiaoxi River supplies26.8 � 108 m3 water into the Taihu Lake (27.7% of the water vol-ume of the Lake). There are two large reservoirs (i.e., Fushi and Lao-shikan reservoirs) in the upstream area of the basin, which areprimarily used for flood control in rainy season. Since the objectiveof our study was to assess the urbanization effect on hydrologicalfluxes, and as most of the upstream forest is native with little landcover change, the region between the Hengtangcun station and thetwo reservoirs was chosen for the detailed study (deep color regionin Fig. 1). The daily observed outflow data of the two reservoirswere taken as the inflow to the downstream area and the daily ob-served streamflow data of Hengtangcun station was used for mod-el calibration.

2.2. Historical land cover change assessment

The change of a single land type percentage (PAi) and a transi-tion matrix (Cij) were used to assess the internal conversion of dif-ferent land cover types, and the two variables are given as follows:

PAi ¼ ðAkþ1i � Ak

i Þ=Aki ð1Þ

Cij ¼

c11 c12 c13 c14c21 c22 c23 c24c31 c32 c33 c34c41 c42 c43 c44

26664

37775 ð2Þ

where Aki and Akþ1

i are the areas of land cover type i in periods k andk + 1, respectively; Cij denotes the area of land type i in period k con-verted to land type j in period k + 1, which was created by spatialanalyst toolbox available with ESRI ArcGIS and regular spreadsheetsoftware (Pang et al., 2010).

2.3. Future urbanization scenarios establishment

The CLUE-S model (the Conversion of Land Use and its Effect atSmall regional extent) was coupled with the SWAT model to sim-ulate the effects of future urbanization scenario based on historicalland use change tendency. The CLUE-S model was developed tosimulate land use change using empirically quantified relationsbetween land use and its driving factors in combination with

Fig. 1. Location map and observed sites of the basin.

F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125 115

dynamic modeling of competition between land use types (Ver-burg et al., 2002). The CLUE-S model includes three modules: grossdemand control module, spatial analysis module and spatial allo-cation module, assuming that the relationship between land useprocess and driving force stays unchanged in the near future. Thisapproach can predict and simulate the spatial expression of landuse changes in the near future by the regression relationship be-tween historical land use patterns and the driving force.

The CLUE-S model has been widely used in international re-searches and many scholars have conducted simulation by control-ling different scenario settings and the total demand for thecorresponding land patterns. In this study, the Markov Chain wasused to predict future urbanization scenarios based on the transi-tion matrix from 2002 to 2008 (Muller and Middleton, 1994).The spatial land use change was simulated with the CLUE-S modelbased on the land suitability factors of elevation, slope, aspect, soiltype, distance to provincial road, distance to river, and distance totown.

The accuracy assessment of model results was based on theKappa coefficient which is used to compare the reference map withthe simulated map or to compare two reference maps. The collec-tion known as Kappa coefficient comes from the notion initiated byScott (1955) that the observed cases of agreement between twomaps include some cases for which the agreement was by chancealone, and the form of the definition of a Kappa coefficient is asfollows:

Kappa ¼ ðPo � PcÞ=ðPp � PcÞ ð3Þ

where Po is the observed proportion correct, Pc is the expected pro-portion correct due to chance, and Pp is the proportion correct withperfect match between two maps, if the agreement between twomaps is perfect, then Kappa = 1. The simulated land cover map of2008 was compared to the actual land use map in 2008; the resul-tant kappa coefficient was 0.82, which means that the calibratedCLUE-S model can be used to simulate future land cover in the studyarea (Batisani and Yarnal, 2009).

2.4. Model setup and calibration

2.4.1. SWAT modelSWAT is a continuous and spatially distributed model designed

to predict the impact of land management practices on water, sed-iment and agricultural chemical yields in large complex water-sheds with varying soils, land use and management conditionsover long periods of time (Neitsch et al., 2002). In the applicationof SWAT model, the study basin is first subdivided into subbasinsbased on DEM (digital elevation model) and channel network,and further delineated by smaller modeling units, known as hydro-logic response units (HRUs) according to topography, types of land-use and soil. Routing of water is simulated from the HRUs to thesubbasin level, and then through the stream network to the basinoutlet.

Flow routing through channel system to the gauges (i.e. stream-flow) mainly consists of surface runoff (Qsurf), lateral flow fromunsaturated soil profiles (Qlatf) and baseflow from undergroundstorage (Qgw). A kinematic storage model which accounts for vari-ation in conductivity, slope and soil water content, is used to pre-dict lateral flow in each soil layer. The model was developed bySloan et al. (1983) and summarized by Sloan and Moore (1984)as below:

Qlatf ¼ 0:024� 2 � SWly;excess � Ksat � slpUd � Lhill

� �ð4Þ

where SWly,excess is the drainable volume of water stored in the sat-urated zone of the hillslope per unit area (mm); Ksat is saturatedhydraulic conductivity (mm/h); Lhill is the hillslope length (m); slpis slope of the land; and Ud is drainable porosity of the soil layer.

SWAT differentiates the underground storage into two portions,shallow aquifer and deep aquifer. The shallow aquifer receives re-charge, i.e., percolation from the unsaturated soil profile. An expo-nential decay weighting function is utilized to account for the timedelay in aquifer recharge once the water exits the soil profile, while

116 F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125

it assumes that water entering the deep aquifer is not consideredin the future water budget calculations and can be considered aslost from the system. The baseflow (i.e., groundwater in the SWAToutput) was generated from the shallow aquifer as following:

Q gw;i ¼ Qgw;i � 1� expð�agw � DtÞ þWrchrg;i � ½1� expð�agw � DtÞ�ð5Þ

where Qgw,i is baseflow from the shallow aquifer on day i (mm/day);agw is the baseflow recession constant; D t is the time step length;Wrchrg,i is the amount of recharge entering the shallow aquifer onday i (mm/day).

In the present study, surface runoff was predicted from dailyrainfall by the Soil Conservation Service (SCS) curve number meth-od based on land use and soil data in the study area. The Muskin-gum method was used for channel flow routing, and the Penman–Monteith method was selected to calculate potential evapotranspi-ration. A more detailed description of the model and its underlyingconceptualizations and parameters refer to the SWAT TechnicalManual (Neitsch et al., 2002). The basin was delineated into 15sub-basins with a threshold area of 1100 ha based on the digitalelevation model (DEM) (Fig. 1).

2.4.2. Data preparationThematic maps required by the model included digital elevation

model (DEM), soil types and properties data, land use and land cov-er map of 1985, 2002 and 2008, and the observed hydro-meteoro-logical data, which are described as follows.

(1) Land cover map. The land cover maps of the study area wereacquired by digitizing the land use maps (1:100,000) in 1985and 2002 (Fig. 2), and were reclassified into four categoriesincluding forest, cropland, urban and water bodies. Landcover in 2008 was from Landsat TM by supervised classifica-tion and manual interpretation after radiometric and geo-metric correction. The overall accuracy of land coverclassification was about 89.5% and the overall Kappa coeffi-cient was 0.86, which showed an acceptable precision. Theland cover and plant growth parameters used in the modelwere estimated using default values in the SWAT usermanual.

(2) DEM. The DEM of the basin was derived from 1:10,000 topo-graphic contour data and then resized to 30 m � 30 m reso-lution for the model input.

(3) Soil. The SWAT model requires different soil propertiesincluding soil texture, available water content, hydraulicconductivity, bulk density and organic carbon content for

Fig. 2. Land use and land cover map

the different layers of each soil type. Spatial soil data withthe resolution of 1:100,000 were obtained from national soilsurvey data map provided by the Anji Bureau of Agriculture,which included yellow soil, red soil, alluvial soil, yellow–redsoil, paddy soils, endodynamorphic soil and eroded red soil.The dominate soil type is yellow–red soil which accounts for49.21% of the study area follows by paddy soil and yellowsoil which account for 21.18% and 11.53%, respectively.

Contents of sand, clay and silt for soil layers were convertedaccording to SWAT criteria by cubic spline interpolation (Caiet al., 2003). Soil stratification, soil depths, soil organic matterand maximum rooting depth were from the soil survey report. Bulkdensity and saturated hydraulic conductivity were calculated bythe SPAW model (Soil–Plant–Atmosphere–Water, Saxton and Wil-ley, 2006) developed by Agricultural Research Service, USA. Soilhydrological grouping was carried out according to soil textureand hydraulic conductivity, and the above soil components andproperties were appended to the SWAT model database.

(4) Hydro-meteorological data. The hydro-meteorological datawere provided by Anji Department of Hydrology and Mete-orology including daily runoff, rainfall, temperature, maxi-mum temperature, minimum temperature, wind speed andrelative humidity data from 1972 to 2009, and the hydrolog-ical database has been scrutinized on a routine basis beforepublication. Nine daily observed rain storms with differentmagnitude peak discharges and peak feature (single ormulti-peak) were selected to assess the effect of urbaniza-tion on storm runoff events over various land cover scenar-ios. Annual streamflow analyzed in this paper was from theHengtangcun hydrological station (the outlet of thewatershed), and the areal rainfall was calculated by ThiessenPolygon method from neighboring gauges located in thestudy area. The location of the hydrological stations, rainfallgauges and weather stations are shown in Fig. 1.

2.4.3. Model calibration and validationHydro-meteorological data of 1983–1987 and land-cover map

of 1985 were used to calibrate model parameters; the land-covermap of 2002 with climate data of 1999–2004 and land-covermap of 2008 with climate data of 2007–2009 were used for modelvalidation.

In consideration of difficulties in the measurement of baseflow,the digital filter-based program was widely used for model calibra-tion (Luo et al., 2012; Yang et al., 2003). In the present study, an

of Xitiaoxi basin (1985–2008).

F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125 117

automatic baseflow filter program was used to separate baseflowand direct runoff (the total amount of surface runoff and lateralflow) from the daily streamflow records (Arnold et al., 1995,http://swat.tamu.edu/software/baseflow-filter-program/), and thetotal runoff was firstly calibrated, then for direct runoff and base-flow calibration in order of yearly, monthly and daily basis untilacceptable results were obtained.

Through One-factor-At-a-Time (LH-OAT) global sensitivity anal-ysis procedure embedded in SWAT model (Griensven and Meixner,2006), the seven most sensitive parameters of the SWAT modelwere identified including CN2 (Curve Number), SOL_AWC (watercapacity of soil layers), ESCO (soil evaporation compensation fac-tor), GWQMN (threshold depth of water required in a shallowaquifer for return flow to occur), Alpha_BF (baseflow alpha factor),SURLAG (surface runoff lag time) and SOL_K (saturated hydraulicconductivity). Nash–Sutcliffe efficiency (Ens) and Correlation coef-ficient (R) were used to evaluate the performance of the model.When Ens approaches 1.0, the model simulates the measured dataperfectly, and when Ens is negative the model is a worse predictorthan the measured mean value. The equation for Ens is as follows:

Ens ¼ 1:0�Xn

i¼1

ðQ si � QoiÞ2Xn

i¼1

ðQ si � Q oÞ2,

ð6Þ

where Qoi and Qsi are the ith observed and simulated streamflows attime i, Qo is the mean observed data over the simulation period, andn is the total number of observations.

The annual and monthly calibration results of the model wereshown in Table 1 and Fig. 3. For the calibration period of 1983–1987 at monthly level, Ens = 0.97 and R = 0.98; for the validationperiod of 1999–2004, Ens = 0.95 and R = 0.97; and for the valida-tion period of 2007–2009, Ens = 0.94 and R = 0.97. Model perfor-mance for the daily calibration and validation period were lessfavorable but still reasonable with average Ens = 0.86 and R = 0.92.

The calibrated and validated hydrographs of nine historicalrainstorm events with various magnitude and process were shownin Fig. 4, where four events were selected for calibration and otherevents were used for validation. Four evaluation criteria are used inmodel evaluation including Ens, the deviation of flood volumes (Dv,the total amount water in the flood), the deviation of peak dis-charge (Dp) and error of time to peak (4T). The equations for Dv,Dp and 4T are as follows:

Table 1Evaluation criteria for discharge simulation during calibration and validation periods.

Annual average value

R Ens

Calibration (1983–1987) 0.97 0.92Validation (1999–2004) 0.99 0.99Validation (2007–2009) 0.99 0.98

Fig. 3. Comparison of simulated and observed monthly streamflow in the calibrat

Dv ¼XN

i¼1

Q si �XN

i¼1

Q oi

! XN

i¼1

Q oi

,ð7Þ

Dp ¼ ðQsp � Q opÞ=Q op ð8Þ

DT ¼ Tsp � Top ð9Þ

where Qsp and Qop are the peak discharges of the simulated and ob-served hydrographs over the simulation period, Tsp and Top are thetime for the observed and simulated hydrograph peaks to arrive,and N is the total number of time steps.

A comparison of the simulated and observed runoff hydro-graphs using the above four criteria was shown in Table 2. Overthe calibration period, the Ens values are ranging from 0.78 to0.96 with an average value of 0.88. The Dp has an average absolutevalue of 12.7 and Dv has an average absolute value of 7.9. The cal-ibrated parameters were validated for the other five historical rain-storm events, and the model performance was found acceptable.

The calibration and validation of the model for different timesteps (i.e. annual, monthly, daily and storm runoff events) provideacceptable results, therefore, the model is capable of predictingstreamflow in different land cover scenarios in the study basin.

3. Results and discussion

3.1. Land cover change

The areas of different land cover and their changes are listed inTable 4 and illustrated in Fig. 2, and the conversion matrices ofland-use changes in total area are summarized in Fig. 5. The landcover in the study area can be categorized into four types, i.e. forestland (including small portion of grass), cropland, urban land andwater body. Spatially, the massif area in the upper reaches ismainly covered by forest, the hilly area in the middle reaches byforest and cropland, while the plain area in the lower reaches byurban and cropland (Fig. 2).

From 1985 to 2002, land cover changes can be summarized asincreases in urban areas by 80.6%, but decreases in forest and crop-land by 2.1% and 11.9%, respectively. The total conversion area ac-counted for 17.3% of the entire area, and the predominant trendwas conversion of cropland to forest and forest to cropland, which

Monthly average value Daily value

R Ens R Ens

0.98 0.97 0.93 0.950.97 0.95 0.92 0.840.97 0.94 0.90 0.79

ion period (1983–1988) and validation periods (1999–2004 and 2007–2009).

Fig. 4. Comparison of observed and simulated hydrographs for the selected nine storm rainfall events.

Table 2Evaluation criteria for the selected nine rainfall events during calibration and validation periods.

Period Land use scenarios Flood no. Observed flood volume (mm) Observed peak discharge (m3/s) Dv (%) Dp (%) Ens 4T

Calibration 1985 198406 417.2 1130 �3.7 �8.1 0.94 0198409 218.7 611 �3.4 �8.9 0.96 0198604 80.4 240 �1.6 21.8 0.78 0199009 309.0 1070 22.7 �12.1 0.82 0Average absolute value 7.9 12.7 0.88 0

Validation 2002 199906 744.2 807 �8.3 8.9 0.89 0200108 236.3 348 �8.3 �6.5 0.89 0

2008 200710 191.1 888 13.7 2.1 0.92 0200806 412.4 700 �9.6 �7.5 0.81 0200908 409.8 748 �26.2 �2.2 0.83 0Average absolute value 13.2 5.4 0.89 0

Table 4Variation in annual runoff depth (mm) over different land use scenarios in different hydrological years.

Hydrologicalyears

Simulated year runoff depth in 1985 Scenario(mm)

Percentage increase (%)

1985–2002 2002–2008 2008-Scenario20

Scenario 20–Scenario25

Scenario 25–Scenario30

Wet (1983) 1217 0.3 0.5 1.3 1.8 0.4Average (2002) 815 0.8 0.6 2.2 1.5 1.4Dry (2006) 415 3.1 2.3 5.0 3.5 3.2

1985–2002 Means the percentage increase from 1985 to 2002 scenarios.

Table 3Characteristics of land use changes in Xitiaoxi watershed from 1985 to 2008.

Land Use Area (km2) Percent (%) Change rate (%)

Class number Name 1985 2002 2008 1985 2002 2008 1985–2002 2002–2008 1985–2008

1 Forest 505.6 495.1 475.6 70.2 68.7 66.0 �2.1 �3.9 �5.92 Cropland 168.0 147.9 133.9 23.3 20.5 18.6 �11.9 �9.4 �20.33 Urban 37.8 68.3 105.0 5.2 9.5 14.6 +80.6 +53.8 +177.84 Water body 9.0 9.2 5.9 1.3 1.3 0.8 +1.4 �36.0 �35.1

118 F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125

Fig. 5. Percentage of land use change types in total area and selected sub-basins from 1985 to 2008. (The numbers 1, 2, 3, 4, represent the forest, cropland, urban and water,respectively; e.g. 12 means transition from land use 1 to land use 2).

Fig. 6. Wet/average/dry distribution of Hengtangcun station (1972–2009).

F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125 119

accounts for 4.9% and 5.9% of the entire area, respectively; and therest is the conversion of forest and cropland to urban area.

From 2002 to 2008, the region had witnessed the most rapid ur-ban development, and the most noticeable changes in land use wasconversion of cropland and forest to urban with the total conver-sion area accounted for 11.6% of entire area. In terms of the changerate, the urban has undergone the biggest changes by 53.8% (from68.3 km2 in 2002 to 105.0 km2 in 2008), following by water bodywith a decreasing rate of �36.0%, agriculture by �9.4% and forestby �3.9%, respectively (Table 3).

Comparison of the land cover maps for 1985 and 2008 revealsthat the urban part of the catchment increased for about 10% inthe study period. The spawn of the urban was more obvious after2002 with an average annual growth rate of 0.85%, which wasabout three times of the previous period. Spatially, the changewas more drastic in the lower reaches close by the city, and thechanges were mainly caused by the rapid urbanization with the in-crease in population. In general, the change can be summarized asincreases in urban area and decreases in agriculture and forest.There are four dominating conversion types, i.e. from cropland tourban, from forest to urban, and mutual conversion of the forestand cropland. Area of land cover change type was shown inFig. 5, and it illustrated that the increasing in urban area wasmainly gained from the decreases in cropland, which representedthe regional urbanization development pattern.

The hydrological response to historical change in land coverespecially urbanization and its impacts at sub-basin level will bediscussed in Section 3.3. For spatial analysis, the whole area wasdivided into 15 sub-basins by ArcSAWT, and three typical sub-ba-sins (S4, S7 and S15) were selected (Figs. 1 and 5), representing dif-ferent conversion patterns and urbanization degrees. Thedescription of the sub-basins is as following:

(1) S4: The sub-basin is a typical suburban basin whichaccounts for 7.5% of the study area, and the basin had under-gone a drastic urbanization from 8.0% in 1985 to 39.3% in2008. The change area accounted for 43% of the sub-basinarea, and the conversion area of cropland to urban and forestto urban accounts for 25.1%, and 5.7% respectively. The con-version area of the cropland to forest and forest to cropland

were nearly the same which accounted for 5.8% and 5.2%,respectively, and the remaining conversion proportion wasless than 2%.

(2) S15 and S7: The sub-basin 15 (S15) was selected as a basinin natural condition which accounts for 5.3% of the studyarea, and the total conversion area accounts only around3% of the sub-basin area. The sub-basin 7 (S7) accounts for8.9% of the study area, and the sub-basin was selected formoderate urban development condition; the increase inurban area was from 4.3% in 1985 to 8.5% in 2008 whichwas gained from cropland. The total area of the forest hadnearly no change.

3.2. Characteristics and long-term change of streamflow, rainfall andtemperature

The coefficient of variation of annual runoff (Cv) and annual ex-treme ratio (Ae = maximum value/minimum value) are used to as-sess the annual streamflow variations. The calculated Cv and Ae is0.34 and 3.94, respectively, which indicates that there was an obvi-ous interannual variation in the annual runoff. We also groupedwet and dry years based on interannual variations of streamflowas follows (Hu, 2000):

P ¼ ðQi � �QÞ=�Q � 100% ð10Þ

where P is anomaly percentage, Qi is annual runoff in year i, and �Q isthe average of annual runoff. When P > 20%, it is a wet year,�20% < P 6 20% is an average year and P < �20% is a dry year.

Fig. 7. Annual hydrological fluxes variations in different land use conditions from 1985 to 2008. Bar graph shows the annual values in the typical year, and lines show thechange rates for the specified period.

120 F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125

During 1972–2009, the wet year period was short (only 1–3 years),while the dry year period usually lasted longer with maximum va-lue of 5 years. The basin was mainly in the wet period during the1980s, and in dry period after 2000s (Fig. 6). This result is used toinvestigate the difference in effects of urbanization on hydrologicalfluxes in different hydrological years as discussed in details inSection 3.3.2.

The results of simple linear regression also indicated a slight de-crease in the annual average streamflow series by 0.1 m3/s everyyear, a decrease in rainfall by �3.3 mm/yr and an increase in tem-perature by 0.05 �C/yr over the period 1972–2009. The distributionof annual total streamflow in the study area was mainly controlledby rainfall, which is dominated by the Asian summer monsoon,and the result revealed a high positive correlation coefficient(R = 0.84) between the annual streamflow and rainfall, and a slightnegative correlation between the streamflow and temperature(R = �0.47). While the annual runoff coefficient shows nearly nochange by �0.003/yr over the period, it might due to the fact therainfall played a dominated role in the variation of streamflow asfar as the whole basin is concerned, as well as the uncertainty inspatial distribution of rainfall and areal rainfall calculation by Thi-essen Polygon method. The slight decreasing trend in runoff coef-ficient might be attributed to that the basin was mainly in dryperiod after 2000. However, the historical land cover change anal-ysis conducted in Section 3.1 showed that the region also hadundergone significant urbanization, and the impact of urbanizationon storm runoff events and on hydrological fluxes at different spa-tial and temporal scales will be examined in the following sections.

3.3. Impacts of urbanization on long-term hydrologic components

3.3.1. Hydrological impact on annual and monthly streamflow at theentire basin level

Based on the SWAT model, the long-term hydrological responseto the land cover change was investigated by fixing the climaticconditions and changing the land cover scenarios during the period1983–2009. The change rate in annual streamflow, surface runoffand evapotranspiration (ET), lateral flow and baseflow were shownin Fig. 7. It illustrated that the annual streamflow and surface run-off increased, while the ET, lateral flow and baseflow decreased dueto the land cover change. Surface runoff and baseflow were foundmore sensitive to land cover change than total streamflow, lateralflow and ET. The effects of land cover change from 1985 to 2008would increase surface runoff by 11.3% and decrease baseflow by�11.2%, while the change rates in streamflow and ET were within2%. The increase in annual streamflow and surface runoff might be

attributed to the fact that ET decreased with lower infiltration rateand soil moisture storage capacity due to the increase in impervi-ous surface.

In terms of monthly variations of hydrological fluxes, thechange in streamflow was more pronounced during the wet season(from May to August), while a relative smaller change in dry sea-son. For example, there was an increasing rate of 2.5% in Juneand nearly no change in November (Fig. 8). The change in ET wassimilar with change trend of streamflow but with different sign,and it has a decrease by 2.5% in June. In terms of surface runoff,there was a higher increase in wet seasons (from May to August),and the largest increase was found in June. The seasonal variationin baseflow and lateral flow was small.

3.3.2. Changes of hydrological fluxes in various hydrological conditionsIn order to investigate the changes of hydrological fluxes in dif-

ferent hydrological years, 2002 (P = 50%), 1983 (P = 90%) and 2006(P = 10%) were chosen to represent average, wet and dry years,respectively. Fig. 9 shows the effects of land use change on hydro-logical fluxes differed in different hydrological years. The annualincrease (decrease) in surface runoff (baseflow) was much higherin wet years than that in dry or average years, while ET had a largerdecrease in dry years. The increase of annual streamflow in the dryyears was much higher than that in the average or wet year. In thedry year, land use and land cover change (LUCC) from 1985 to 2008increased the annual total runoff depth by 23 mm (5.5%); in theaverage year by 12 mm (1.4%), while in the wet year by 9 mm(0.8%).

3.3.3. The relationship of hydrologic response and land use change atsub-basin level

The spatial impacts of LUCC at sub-basin level were analyzed bythe correlation analysis of urbanization and change of hydrologicalfluxes in 15 subbasins over the periods 1985–2002 and 2002–2008. The results indicated that urbanization had a high correla-tion with the streamflow, surface runoff and baseflow, with thecorrelation coefficient (R) of 0.95, 0.84 and �0.89, respectively,while the urbanization rate had a relatively lower correlation withET (R = �0.49) and lateral flow (R = �0.20). The changing rate in thehydrological fluxes at subbasin level was coincided with changingrate of urbanization. For the selected sub-basins, the subbasin S4had the largest urbanization rate (from 8.0% in 1985 to 39.3% in2008) caused the largest change in the hydrologic components,where the annual surface runoff increased by 23.2%, the baseflowdecreased by 30.3%, total streamflow increased by 5.7% and ET de-creased by 2.4%, respectively. The selected subbasin S7 had a small

Fig. 8. Monthly changes in hydrological fluxes due to land cover change from 1985 to 2008.

Fig. 9. Box-plot of annual variations of hydrologic fluxes in different hydrological years.

F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125 121

increase in urban area from 4.3% in 1985 to 8.5% in 2008 which wasmainly gained from the cropland, the change in surface runoff andbaseflow was within 10%, and the change rates in total streamflowand ET were less than 2%. While the nature basin S15 (with almost

no land use change) had a subtle change in all hydrologiccomponents.

The spatial distribution of the changes in hydrological fluxeswas shown in Fig. 10, where remarkable spatial variations of the

Fig. 10. The percentage increase in total runoff depth and urbanization in 2008 relative to 1985 for different hydrological years: (A) the increase in urban area, (B) wet year,(C) average year, (D) Dry year) at the subbasin level.

122 F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125

hydrological fluxes can be detected. The figure indicated that thehydrological impacts of land use change would be most pro-nounced in suburban areas in dry years.

3.4. Effect of urbanization on storm runoff events

In order to investigate the different impacts of urbanization onstorm runoff events with various magnitudes, the selected ninestorm runoff events were ranked in ascending order in Fig. 11. Itwas found that urbanization increased the flood peak dischargeand volume for all the storm runoff events, and the change ratesare dependent on the magnitude of the storm runoff events. Thechange in land cover from 1985 and 2008 increased the flood vol-ume (by 0.7–2.3%) and the peak discharge (by 1.6–4.3%), and thechange was more distinct for peak discharge than for flood volume.A closer look at the figure revealed that the change rate was relatedto antecedent 15-day precipitation (A15d) and the sensitivity ofhydrologic response to urbanization tended to increase as therecurrence interval of the rainfall events decreases, i.e., it is morepronounced for small flood events. For example, the two smallerflood events (i.e. Nos. 198604 and 200108) had higher increase

rates of 4.3% and 3.6% for peak discharge than the other larger floodevents.

For multi-peak flood events, the former rainfall event broughtmore soil moisture and higher groundwater levels which wouldweaken the urbanization impact, e.g., the flood No. 200806 whichhas two flood peaks had a larger effect on the former peak dis-charge with an increase rate of 4.2% comparing to the latter peakdischarge with an increase rate of 2.6%. The same is true for floodNo. 199906. The results suggested that antecedent soil moistureconditions and groundwater levels played a certain role for the de-gree to which urbanization might influence storm runoffgeneration.

The impacts of distinct changing rates of urban expansion(Fig. 10A) on the subbasins hydrological fluxes are shown inFig. 12, which indicated that hydrological impacts would be mostpronounced in suburban basin. Taking the selected subbasins asan example, the suburban basin (S4) had a highest increment inurban land use from 1985 to 2008, and the increases in peakdischarge and flood volume were from 5.1% to 10.6% and from4.9% to 10.1%, respectively. While subbasin 7 (S7) showed a smallincrease in peak discharge and flood volume by around 3%. The

Fig. 11. Percentage increase in peak discharge and flood volume over the land cover change period (A15d is antecedent 15-day precipitation; 1985–2002 means the changefrom 1985 to 2002 scenarios).

Fig. 12. Percentage increases of peak discharge and flood volume in 2008 relative to 1985 for flood No. 198406 at the subbasin level.

F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125 123

subbasin with natural condition (S15) showed nearly no change inflood volume and peak discharge. Comparison of the impact be-tween the storm events and annual streamflow analyses (Figs. 10and 12) shows the different impact at subcatchment level, whichmight be attributed to the fact the uncertainty in spatial distribu-tion of rainfall would have more impact on the assessment ofannual hydrological process than for storm event.

3.5. Hydrological impacts of future urbanization scenarios

The analysis of the historical land cover change showed the per-centage of the urban area was around 5%, 10% and 15% in 1985,2002, and 2008 respectively. To further study the urbanizationimpact and provide a reference for urbanization development inthe near future, the calibrated CLUE-S model was executed for

Fig. 13. Variation of peak discharge (m3/s) and flood volume (mm) for Flood 198406 (20-year flood) and flood 200710 (10-year flood).

124 F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125

the following three scenarios with an urbanization rate of 20%(Scenario 20), 25% (Scenario 25) and 30% (Scenario 30), respec-tively. The corresponding changes of hydrological fluxes due tothe urban expansion were summarized in the bar-diagram ofFig. 13 and Table 4, which showed that, the higher urbanizationwould be able to generate more runoff. For the average year, landuse change from 1985 to Scenario 30 tended to increase the annualrunoff depth by 55 mm (6.7%), and the largest increase occurredover the period from 2008 to Scenario 20, followed by the periodfrom Scenario 20 to Scenario 25; and similar result was found inwet or dry years. The future urbanization would cause a greaterflood risk. Taking the 20–year flood as an example (Flood 198406in Fig. 13), change in land use from 1985 to Scenario 30 would in-crease peak discharge by 70.7 m3/s (7.2%) and flood volume by8.8 mm (2.4%), and the largest increase in peak discharge wasfound over the period from Scenario 20 to Scenario 25.

Generally, the modeling results showed the impacts of urbani-zation on runoff would be obvious when the percentage of urbanarea changes from 15% to 30% under the current land managementpolicy; which suggested that more attention needs to be paid onriver basin management and flood control in future urban develop-ment in the study area.

4. Conclusions

To investigate the impacts of rapid urbanization on hydrologicalfluxes in the Yangtze River Delta, this paper conducted the casestudy in Xitiaoxi basin in the lower reach of the Yangtze River byutilizing GIS, RS and SWAT model. The following conclusions canbe drawn.

� Impacts of urbanization on hydrological fluxes were different indifferent seasons and hydrological years. The urbanization hadless impact on annual total runoff than on surface runoff andbaseflow. The increase in surface runoff caused by urban expan-sion was higher in wet years or in the wet seasons than in dryyears or dry seasons.� As for the storm runoff events, the urbanization increased peak

discharge more than that of flood volume, and the change ratewas found higher for small flood events than for large events.As for multi-peak flood events, urbanization caused moreincrease on former flood peak than on the latter flood peaks.� Urbanization and its impacts on hydrological fluxes can be better

understood at the sub-basin level. Suburban area might experi-ence high flood risk as urbanization develops even though theimpact at the whole basin level might be not remarkable.� The coupling of CLUE-S and the SWAT model was found to be a

good approach in quantitatively evaluating the future urbaniza-tion impact on hydrological fluxes.

The study approach will provide useful information and refer-ence for similar studies to be conducted in other regions, and theresults will have useful implications for flood mitigation and waterresources planning and management in the region.

Acknowledgments

This research is supported by the key program of National NaturalScience Foundation of China (Grant No. 40730635), the Commonwealand Specialized Programs for Scientific Research, Ministry of WaterResources of China (Grant Nos. 201201072, 201301075).

F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125 125

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