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Assessing the effects of urbanization on annual runoff and flood events usingan integrated hydrological modeling system for Qinhuai River basin, China

Jinkang Du a, Li Qian a, Hanyi Rui a, Tianhui Zuo b, Dapeng Zheng a, Youpeng Xu a, C.-Y. Xu c,⇑a School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, Chinab Earthquake Administration of Guangxi Antonomous Region, Nanning 530022, Chinac Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, NO-0316 Oslo, Norway

a r t i c l e i n f o

Article history:Received 22 July 2011Received in revised form 7 June 2012Accepted 30 June 2012Available online 20 July 2012This manuscript was handled byKonstantine P. Georgakakos, Editor-in-Chief,with the assistance of Timothy DavidFletcher, Associate Editor

Keywords:CA-Markov modelHEC-HMS modelUrbanizationAnnual runoffPeak flowFlood volume

s u m m a r y

This study developed and used an integrated modeling system, coupling a distributed hydrologic and adynamic land-use change model, to examine effects of urbanization on annual runoff and flood eventsof the Qinhuai River watershed in Jiangsu Province, China. The Hydrologic Engineering Center’sHydrologic Modeling System (HEC-HMS) was used to calculate runoff generation and the integratedMarkov Chain and Cellular Automata model (CA-Markov model) was used to develop future land usemaps. The model was calibrated and validated using observed daily streamflow data collected at thetwo outlets of watershed. Landsat Thematic Mapper (TM) images from 1988, 1994, 2006, Enhanced The-matic Mapper Plus (ETM+) images from 2001, 2003 and a China–Brazil Earth Resources Satellite (CBERS)image from 2009 were used to obtain historical land use maps. These imageries revealed that thewatershed experienced conversion of approximately 17% non-urban area to urban area between 1988and 2009. The urbanization scenarios for various years were developed by overlaying impervious surfacesof different land use maps to 1988 (as a reference year) map sequentially. The simulation results of HEC-HMS model for the various urbanization scenarios indicate that annual runoff, daily peak flow, and floodvolume have increased to different degrees due to urban expansion during the study period (1988–2009),and will continue to increase as urban areas increase in the future. When impervious ratios change from3% (1988) to 31% (2018), the mean annual runoff would increase slightly and the annual runoff in the dryyear would increase more than that in the wet year. The daily peak discharge of eight selected floodswould increase from 2.3% to 13.9%. The change trend of flood volumes is similar with that of peak dis-charge, but with larger percentage changes than that of daily peak flows in all scenarios. Sensitivity anal-ysis revealed that the potential changes in peak discharge and flood volume with increasing impervioussurface showed a linear relationship, and the changes of small floods were larger than those of largefloods with the same impervious increase, indicating that the small floods were more sensitive than largefloods to urbanization. These results suggest that integrating distributed land use change model and dis-tributed hydrological model can be a good approach to evaluate the hydrologic impacts of urbanization,which are essential for watershed management, water resources planning, and flood management forsustainable development.

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1. Introduction

The world population has grown very rapidly over the last150 years and continues to do so, resulting in impacts on hydro-logic resources at both a local and global scale. One of the recentthrusts in hydrologic modeling is the assessment of the effects ofland use and land cover changes on water resources and floods(Yang et al., 2012), which are essential for planning and operationof civil water resource projects, and for early flood warning. Theinfluence of urbanization as one of the important land use and land

cover changes on runoff and floods within watersheds is one of themain research topics in the past decades.

It is widely recognized that urbanization changes hydrologicalprocesses within watersheds by altering surface infiltration char-acteristics. The expected results of urbanization include reducinginfiltration, baseflow, lag times, increasing storm flow volumes,peak discharge, frequency of floods, and surface runoff (Hollis,1975; Arnold and Gibbons, 1996; Smith et al., 2005; Doughertyet al., 2006; Ogden et al., 2011). Numerous researchers have usedmany methods to simulate, assess, and predict the effects of urban-ization on hydrological response of the watersheds. For example,Tung and Mays (1981) developed a non-linear hydrological sys-tem-state variable model to simulate urban rainfall–runoff, and

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⇑ Corresponding author. Tel.: +47 22 855825; fax: +47 22 854215.E-mail address: [email protected] (C.-Y. Xu).

Journal of Hydrology 464–465 (2012) 127–139

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examined the variation of each parameter for different levels ofurbanization. Bhaskar (1988) adopted Clark’s instantaneous unithydrograph concept to determine the parameters that influencethe effect of urbanization on the watershed. Ferguson and Suckling(1990) applied polynomial regressive equations of impervioussurfaces to analyze the relationship of runoff to rainfall for total an-nual flows, low flows and peak flows. Kang et al. (1998) illustratedthe runoff characteristics of urbanization by utilizing the conceptof linear cascading reservoirs. Valeo and Moin (2000) used a modelcalled TOPURBAN, a revision of TOPMODEL, to observe the interac-tion between parameters on urbanized watersheds. Cheng andWang (2002) developed a method to define the degree of changein runoff hydrographs for the urbanizing Wu-Tu watershed inTaiwan. Choi et al. (2003) applied the Cell Based Long Term Hydro-logical Model (CELTHYM) to evaluate long term hydrologic impactscaused by land use changes associated with urbanization for a wa-tershed in central Indiana. Huang et al. (2008) used regressionanalysis to establish the relationship between hydrograph param-eters and peak discharge and their corresponding imperviousnessfor the urbanizing Wu-Tu watershed in Taiwan. Franczyk andChang (2009) used an ArcView Soil and Water Assessment Tool(AVSWAT) hydrological model to assess the effects of climatechange and urbanization on the runoff of the Rock Creek basin inthe Portland metropolitan area, Oregon, USA. Lin et al. (2009) as-sessed the impact of land-use patterns on runoff in watershedand sub-watershed scales for an urbanized watershed in Taiwanby combined use of a spatial pattern optimization model (OLPSIM),the Conversion of Land-Use and its Effects model (CLUE-s) and theHydrologic Engineering Center’s Hydrologic Modeling System(HEC-HMS). Im et al. (2009) applied the MIKE SHE model to quan-titatively assess the impact of land use changes (predominantlyurbanization) on hydrology of the Gyeongancheon watershed inKorea. Li and Wang (2009) used a Long-Term Hydrologic ImpactAssessment (L-THIA) model to evaluate the effect of land use andland cover change on surface runoff in the Dardenne Creek wa-tershed of St. Louis, Missouri. Chu et al. (2010) used the Conversionof Land-use and its Effects (CLUE-s) model and Distributed Hydrol-ogy-Soil Vegetation Model (DHSVM) to examine hydrologic effectsof various land-use change scenarios in the Wu-Tu watershed innorthern Taiwan.

Distributed models rely on a physically based description of therunoff generation and the effects of different land covers play animportant role in exploring hydrologic effects of land-use changesin the catchment. The above-mentioned Mike SHE, SWAT,HEC-HMS, DHSVM, L-THIA and CELTHYM, for example, have beenextensively used to assess the effects of land use changes (predom-inantly urbanization) on hydrologic processes. However, most dis-tributed models are commonly used in small watersheds with asingle-outlet, and in our study area, the Qinhuai River basin hastwo outlets (bifurcation—a split in the flow in a channel), a suitabledistributed model that can deal with such basins needs to be se-lected and evaluated. The HEC-HMS is one such model and there-fore was selected together with a land-use change model toexplore the hydrological effect of urbanization in the Qinhuai Riverbasin.

Many methods have been developed to simulate land usechange, such as empirical–statistical models, stochastic models,conceptual models, and dynamic (process-based) models (Lambinet al., 2000). Among those, Markov Chain and Cellular Automatamodels are most often used. Markov chain models are commonlyused to quantify transition probabilities of multiple land cover cat-egories from discrete time steps; however, there is no spatial com-ponent in the modeling outcome. Cellular Automata (CA), on theother hand, can effectively model proximity to predict spatially ex-plicit changes over a certain period of time (Balzter et al., 1998;Clark-Labs, 2003). The CA-Markov model is the combination of

both Markov and CA models, possessing the temporal characterof Markov chain models and the spatial character of CA models.The foundation of a CA-Markov model is an initial distributionand a transition matrix, which assumes that the drivers that pro-duce the detectable patterns of land cover categories will continueto act in the future as they had been in the past (Briassoulis, 2000).In this study, the CA-Markov model was used to develop futureland use change scenarios, and based on which the future urbani-zation scenarios can be constructed.

In this paper, the CA-Markov model and HEC-HMS model systemwere used as an integrated system to quantify the annual runoffand flood response to urbanization. The main objective of this studywas to develop and test the integrated modeling system for analyz-ing the effects of sub-urban development on runoff and flood eventsunder urbanization scenarios taken from multi-temporal satelliteimageries for the Qinhuai River basin in China, which is essentialfor maintaining an adequate water supply, protecting water qualityand management of flood disasters. The study provides a usefulframework for similar studies in other regions of the world. The pri-mary goal was achieved through the following steps: (1) to developan integrated modeling system that couples a distributed hydro-logic model and a dynamic land use change model for examiningthe effects of urbanization on annual runoff and flood events; (2)to propose a method which can be used to develop urbanizationscenarios for determining hydrologic response of watersheds tourbanization; (3) to test the capabilities of HEC-HMS modeling sys-tem for simulating daily stream flow in a large basin (in this case, anarea of about 2600 km2); and (4) to explore whether the effects ofsuburban development on runoff characteristics of the study areaare the same with those widely acknowledged.

2. Materials and methods

2.1. Study area and data

Qinhuai River basin is located between 118�390 and 119�190Elongitude and 31�340 to 32�100N latitude. It has an area of 2631square kilometers, and the elevation ranges from 0 to 417 m,encompassing Nanjing and Jurong cities of Jiangsu Province, China.The basin has experienced dramatic urbanization over the pastdecades, resulting in extensive land use changes. Therefore, it isessential and valuable to assess the hydrologic impacts of landuse changes in the region for the current situation and futurescenarios.

The studied basin lies in the humid climatic region. The meanannual precipitation is approximately 1047 mm, and the rainy sea-son extends from April to September, with intense precipitation insummer (June to August). The mean annual temperature is about15.4 �C.

The land use types are paddy field, woodland, impervious sur-face, water, and dry land. Among those, paddy field and dry landare the main land use types (for details see Section 3.1). The mainsoil types are yellow–brown soil, purple soil, limestone soil, paddysoil, and gray fluvo-aquic soil.

Seven raingage stations and two stream flow gauging stations atthe outlets of the basin were used for the study. The watershedlocation, elevation, distribution of rainfall and flow gauging sta-tions, and streams are seen in Fig. 1.

The data used in this study were: (a) multi-temporal and multi-spectral satellite images, representing land use changes in the ba-sin over time; (b) daily rainfall data of the seven raingage stationsfor the 21-year period (1986–2006) from the China MeteorologicalData Sharing Service System; (c) daily discharge data of Inner Qin-huai station and Wudingmen station covering the period from Jan-uary 1986 to December 2006; (d) soil map of the study area on

128 J. Du et al. / Journal of Hydrology 464–465 (2012) 127–139


1:75,000 scale; and (e) Digital Elevation Model (DEM) of theQinhuai River basin.

2.2. Generation of historical land use scenarios

As the basis for hydrologic impact evaluation of the land usechanges, digital land use maps were generated from a multi-tem-poral and multi-spectral dataset. Landsat Thematic Mapper (TM)images from 1988, 1994, 2006, Enhanced Thematic Mapper Plus(ETM+) images from 2001, 2003 (all with 30 m resolution), and20 m resolution China–Brazil Earth Resources Satellite (CBERS) im-age from 2009 were used in this study. While the sensors offer dif-ferent spatial and spectral resolutions, such multispectral datasetsare often unavoidable in studies spanning over several decades andhave been successfully applied in other regions (Zoran and Ander-son, 2006).

Image pre-processing was carried out in ERDAS Imagine 9.3.The satellite images were generated by applying coefficients forradiometric calibration, geometric rectification and projected tothe Universal Transverse Mercator (UTM) ground coordinates witha spatial resampling of 30 m. Geometric rectification was carriedout on Landsat images from 1988, 1994, 2003, 2006 and CBERS im-age from 2009 using the ETM+ from 2001 as a base-map, and near-est neighbor resampling algorithm, with root mean square (RMS)error of less than 0.5 pixels via image-to-image registration. Radio-metric calibration and atmospheric correction were carried out tocorrect for sensor drift, differences due to variation in the solar an-gle, and atmospheric effects (Green et al., 2005).

The supervised classification method with maximum likelihoodclustering and DEM data were employed for image classification asa hybrid method to generate land use maps and post-classificationanalysis was applied to create the trend map of land use changes.Land use categories were paddy field, dry land, woodland, impervi-ous surface and water. Pure pixels, rather than mixed pixels, wereselected as training samples. Mixed classes such as paddy field andwoodland were separated with the aid of DEM data. Ground tru-thing was performed to assist in the imagery classification and tovalidate the final results. Each image was classified following thesame method.

Overall accuracy and Kappa value were selected as evaluationcriteria for the classification. An error matrix was generated based

on test samples for each land use map. The columns of error ma-trix represent the reference data by ground truthing, while therows indicate the classified land use category. The overall accu-racy is computed by dividing the total correct pixels (i.e., thesum of the major diagonal) by the total number of pixels in theerror matrix (Russell, 1991). Kappa analysis is a discrete multivar-iate technique used in accuracy assessment, Kappa value (Kap) iscomputed as

Kap ¼NPr

i¼1xii �Pr

i¼1xi � xþi

N2 �Pr

i¼1xiþ � xþi

ð1Þ

where r is the number of rows in the matrix, xii is the observation inrow i and column i, xi+ and x+i are the marginal totals of row i andcolumn i, respectively, and N is the total number of observations(Bishop et al., 1975).

The overall accuracy ranges from 0 to 1, and kappa value is be-tween �1 and 1. If the test samples are in perfect agreement (allthe same between classification results and predicted results), val-ues for the overall accuracy and Kap equal to 1.

In this study, the overall classification accuracy of each imagewas over 89% with kappa values over 0.79, meeting the accuracyrequirements. The selected land use maps were shown in Fig. 2.

2.3. Development of future land use scenarios

The CA-Markov model was used to develop future land usechange scenarios. A Markov chain is a stochastic process that con-sists of a finite number of states of a system in discrete time stepsand some known transition probabilities Pij (the probability ofthat particular system moving from time step i to time step j).The value of the stochastic process at time t, St, depends onlyon its value at time t � 1, St�1, and not on the sequence of valuesSt�2, St�3, . . .,S0. Land use change can be regarded as a stochasticprocess and different categories are the states of a chain. TheMarkov chain equation was constructed using the land use distri-butions at the time step i (Si), and at the time step j (Sj) of a dis-crete time period as well as transition probabilities Pij

representing the probabilities of each land use category changingto every other category (or remaining the same) during that per-iod. Pij equation is as follows:

Fig. 1. Map of Qinhuai River basin used in this study.

J. Du et al. / Journal of Hydrology 464–465 (2012) 127–139 129


Pij ¼

P11 P12 � � � P1n

P21 P22 � � � P2n

..

. ... . .

. ...

Pn1 Pn2 � � � Pnn

266664

377775

0 6 Pij < 1 andXN

i¼1;j¼1

Pij ¼ 1 ði; j ¼ 1;2 � � �nÞ

ð2Þ

Future land use can be modeled on the basis of the precedingstate and a matrix of actual transition probabilities between thestates. However, there is no spatial component in the modelingoutcome. Cellular automata (CA), on the other hand, can effectivelymodel proximity, i.e., areas will have a higher tendency to changeto the land use category of the neighboring cells (Balzter et al.,1998). CA works as a dynamic and spatially explicit modeling ap-proach, in which the state of each cell at time t + 1 is determinedby the state of its neighboring cells at time t according to thepre-defined transition rules. Five components were included: (a)a space composed of discrete cells, (b) a finite set of possible statesassociated to every cell, (c) a neighborhood of adjacent cells whosestate influences the central cell, (d) uniform transition rulesthrough time and space, and (e) a discrete time step to which thesystem is updated (Wolfram, 1984). The hybrid CA-Markov model(Cellular Automata-Markov), integrating the merits of the Markovchain and CA models, can reconstruct the spatial patterns of futureland use based on the quantity prediction of Markov, and therefore,has been shown to improve land use modeling (Pinki and Jane,2010; Li et al., 2010).

In this study, CA-Markov model was performed in the softwareIDRISI (Clark-Labs, 2003). Land use of 2009 has been built with thetrend of land use change during 2003–2006. The detailed proce-dure for developing land use scenarios is presented below.

First, a transition probability matrix, a transition areas matrix,and a collection of conditional probability images were developed

using land use maps (30 m � 30 m spatial resolution) of 2003 and2006 based on Markov module of the software. The transitionprobability matrix is a text file that records the probability of eachland use category changing to every other category. The transitionareas matrix is a text file that records the number of pixels that areexpected to change from each land use type to other land use typeover the specified number of time units. The conditional probabil-ity images report the probability of each land cover type to befound at each pixel after the specified number of time units.

Second, transition suitability image collection was generated,where a number of maps that show the suitability for each landuse category with values are stretched to a range of 0–255. Theprobability maps created by the Markov module were used asthe suitability map.

Third, a 5 � 5 contiguity filter was used to generate a spatial ex-plicit contiguity-weighting factor to change the state of cells basedon its neighbors. The filter emphasized that the spatial scale of150 m � 150 m around a cell would have more significant impactson land use change of the cell.

Fourth, 3-year loops times were used for the CA model to pre-dict land use. Then the land use map of 2009 was developed usingthe land use map of 2006 as the baseline.

The predicted land use map of 2009 (Fig. 2e) was comparedwith the classification of CBERS image from 2009 (Fig. 2d) to testthe model accuracy according to the area of each land use category.The classification of the CBERS image was considered as the actualland use distribution; an error matrix was generated based on 400test samples.

In the same way, with the transition matrix generated between2003 and 2006, a 6-year loop time and a 12-year loop time wereused to predict the land use map of 2012 and 2018 using the landuse map of 2006 as the baseline, respectively.

Fig. 2. The land use maps of the basin.



2.4. Building of urbanization scenarios

In order to analyze hydrological effects of urbanization and ex-clude complicated effects caused by all other land use changes, theurbanization scenarios are built following three steps: first, theland use map of 1988 was chosen as a reference; second, impervi-ous surfaces (urban areas) were extracted from land use maps of1994, 2001, 2003, 2006, 2009, 2012, and 2018; and third, impervi-ous surfaces (urban areas) extracted in step two were overlaid tothe land use map of 1988 to produce urbanization scenarios for1994, 2001, 2003, 2006, 2009, 2012, and 2018 respectively. In sucha way, the urbanization scenarios only differ in the size of urbanareas while the rest of the catchment remain the same land usetype as in 1988. That is to say, there could only be transitions ofother land use types to impervious surfaces, and no inter ex-changes among other land use types within the urbanization sce-nario series, therefore the hydrologic effect of urbanization couldthen be assessed avoiding other effects caused by all land usechanges.

2.5. Development of hydrological soil map

Soil data of the study area were generated from existing SoilSurvey maps at a scale of 1:75,000. Soil maps were rectified andmosaicked, so that the study area was extracted by sub-setting itfrom the full map. Boundaries of different soil textures were digi-tized and various polygons were assigned to represent differentsoil categories such as yellow–brown soil, purple soil, limestonesoil, paddy soil, and gray fluvo-aquic soil. According to the rulesof hydrologic soil group classifications developed by the US NaturalResource Conservation Service (NRCS), only hydrologic soil groupsB (paddy soil, purple soil) and C (yellow–brown soil, limestone soiland gray fluvo-aquic soil) are presented in the basin (Fig. 3), indi-cating a moderate infiltration rate and a slow infiltration raterespectively when thoroughly wetted.

2.6. Description of HEC-HMS

In this study, we used the hydrological model, HEC-HMS, to cal-culate the runoff from the resulting landscapes. HEC-HMS is hydro-logic modeling software developed by the US Army Corps of

Engineers Hydrologic Engineering Centre (HEC). HEC-HMS usesseparate sub-models to represent each component of the runoffprocess, including models that compute rainfall losses, runoff gen-eration, base flow, and channel routing. Each model run combinesthe Basin Model, the Precipitation Model, and the Control Model.The Basin Model contains the basin and routing parameters ofthe model, as well as connectivity data for the basin. The Precipita-tion Model contains the rainfall data for the model. The ControlModel contains all the timing information for the model. The usermay specify different data sets for each model and then the hydro-logic simulation is completed by using of data set for the BasinModel, the Precipitation Model, and the Control Model. The detailsof model structures and various processes involved are given in theTechnical Reference Manual (USACE-HEC, 2000) and the User’sManual (USACE-HEC, 2008) of HEC-HMS. A brief description ofmodels used in this study is provided here for completeness only.

HEC-HMS categorizes all land types and water in a watershed aseither directly connected impervious surface or pervious surface.Precipitation on directly connected impervious surface runs offwith no volume losses. Precipitation on the pervious surfaces issubject to losses (Jha and Mahana, 2010). The SCS-CN loss modelwas used in the present study, which estimates precipitation ex-cess as a function of cumulative precipitation, soil cover, landuse, and antecedent moisture using the following equation (Singh,1994):

Pe ¼ðP � IaÞ2

P � Ia þ Sð3Þ

where Pe is accumulated precipitation excess at time t, P is accumu-lated rainfall depth at time t, Ia is the initial abstraction (initial loss),and S is potential maximum retention, a measure of the ability of awatershed to abstract and retain storm precipitation.

The SCS developed an empirical relationship between Ia and S asIa = 0.2S. Therefore, the cumulative excess at time t is given as:

Pe ¼ðP � 0:2SÞ2

P þ 0:8Sð4Þ

The maximum retention (S) is determined using the following equa-tion (SI system):

S ¼ 25;400� 254CNCN

ð5Þ

where CN is the SCS curve number. It is an index that represents thecombination of hydrologic soil group, land use classes, and anteced-ent moisture conditions.

The Clark unit hydrograph (Clark UH) model has been appliedfor estimating direct runoff. Clark’s model derives a watershedUH by explicitly representing two critical processes in the transfor-mation of excess precipitation to runoff: Translation of the excessfrom its origin throughout the drainage system to the watershedoutlet and attenuation of the magnitude of the discharge as the ex-cess is stored throughout the watershed. Application of the Clarkmodel requires properties of the time-area histogram and a storagecoefficient. The time-area relationship can be represented by asmooth function requiring only one parameter, the time of concen-tration. The storage coefficient is an index of the temporary storage

Table 1Curve number for hydrologic soil groups B and C.

Land use B C

Paddy field 76 84Woodland 64 73Impervious surface 98 98Water 95 95Dry land 76 82

Fig. 3. Hydrologic soil map of the basin.



of precipitation excess in the watershed as it drains to the outletpoint. The two parameters can be estimated via calibration ifgauged precipitation and streamflow data are available or by equa-tions presented in Bedient and Huber (1992).

In HEC-HMS, the baseflow model is applied both at the start ofsimulation of a storm event, and later in the event as the delayedsubsurface flow reaches the watershed channels. The recessionmodel adopted in present study explains the drainage from naturalstorage in a watershed. It defines the relationship of the baseflowQt at any time t to an initial value Q0 as:

Q t ¼ Q 0Kt ð6Þ

where K is an exponential decay constant. A threshold flow, afterthe peak of the direct runoff, should be specified either as a flowrate or as a ratio to the computed peak flow when applying reces-sion model (Jha and Mahana, 2010).

The Muskingum method was adopted to compute outflow fromeach reach. The method uses the following equation:

Q2 ¼ ðc1 � c2ÞI1 þ ð1� c1ÞQ 1 þ c2I2

c1 ¼2� Dt

2� K � ð1� XÞ þ Dt

c2 ¼Dt � 2� K � X

2� K � ð1� XÞ þ Dt

ð7Þ

where I1, I2 are the inflows to the routing reach at the beginning andend of computation interval respectively, Q1 and Q2 are the outflowsfrom the routing reach at the beginning and end of computationinterval respectively, K is the travel time through the reach, X isthe Muskingum weighting factor (0 6 X 6 0.5), and Dt is the lengthof computation interval.

2.7. Construction of HEC-HMS project

The project containing the Basin Model, the Precipitation Modeland the Control Model was created. The Basin Model was builtbased on hydrologic elements such as sub-basin, reach, diversion,junction, reservoir, source and sink, and hydrologic models corre-sponding to each element. The basin and sub-basin boundaries as

Fig. 4. Sketch map of hydrologic elements in Basin Model.



well as stream networks needed by the Basin Model were delin-eated using terrain processing module of ArcHydro Tools softwarebased on DEM data obtained from existing 1:50,000 scale contourmap. The initial values of the model parameters were determinedby using the default values given by HEC-HMS. The land use andsoil maps of the basin were used to assign CN (Curve Number) val-ues to each grid (30 m � 30 m resolution) with the help of HEC-GeoHMS Project View, referring to the standard table providedby SCS-USA (McCuen, 1998). Weighted CN values were calculatedfor each sub-basin with averaging method in the spatial analystmodule of ArcGIS. Curve Numbers ranged from approximately64–98 for all sub-basins in this study area (Table 1). Fig. 4 showsthe hydrologic elements in the Basin Model.

The Precipitation Model was set up by putting in daily rainfalldata for each sub-basin, which were calculated by using nearestneighbor method based on the point rainfall values observed atthe seven raingage stations. The Control Model containing all thetiming information for the model was built by determining timesteps, start and stop date, and times of the simulation.

2.8. Calibration and validation of HEC-HMS

In this study, the HEC-HMS model was calibrated and evaluatedusing a split sample procedure against streamflow data collected atthe outlets of the watershed. The objective of the model calibrationwas to match simulated daily runoff with the observed data withdifferent meteorological conditions and land cover conditions.

In this study, two evaluation criteria, correlation coefficient (R)and model efficiency (E) (Nash and Sutcliffe, 1970) were used to as-sess model performance. To calibrate and verify the HEC-HMSmodel, 21-year (1986–2006) streamflow and precipitation datawere used for the study watershed. The observed runoff datasetwas divided into a calibration period (1986–1998) and a verifica-tion period (1999–2006) based on the land use data years 1988,1994, 2001, and 2006. For model calibration, land use data for1988 and rainfall data for 1986–1992 were used for 1986–1992simulation, and land use data for 1994 and rainfall data for1993–1998 were used for 1993–1998 simulation.

Initial abstraction, time of concentration, storage coefficient,recession constant, baseflow threshold ratio to peak, Muskingumweighting factor and travel time were considered as HEC-HMS cal-ibration parameters. A series of model parameters sets was esti-mated using automated optimization tool provided by HEC-HMSby selecting several objective functions, and model efficiency (E)for whole calibration period was computed for each set of param-eters to examine the calibration results. The calibrated modelparameters were obtained using peak-weighted root mean square

error as the objective function. Validation was then performed;parameters used during calibration were not changed during mod-el validation. HEC-HMS was validated for the 1999–2003 simula-tion using land use data of 2001 and rainfall data of 1999–2003,and for 2004–2006 simulation using land use data of 2006 andrainfall data of 2004–2006.

In order to assess the urbanization effects on flood flow, four-teen flood events with daily peak discharge greater than 500 m3/s and two other smaller flood events during 1986–2006 were se-lected for calibration and validation. Four flood events with differ-ent peak discharges were selected for model calibration. Thecalibration parameters for flood events simulation were same asthose for long-term simulation. The optimized parameter sets foreach calibrated flood events were obtained by selecting peak-weighted root mean square error as the objective function andusing the Nelder and Mead simplex search algorithm provided byHEC-HMS.

3. Results and discussion

3.1. Historical land use change

The land use changes from 1988 to 2009 are presented in Table2. During 1988–2009, paddy field is the main land use type cover-ing over 40% of the total areas, and the second main land use cat-egory is dry land, which occupied over 22%. Subsequently, thewoodland occupied over 15%, with water occupying the remainder.The urban area development has been recognized for over21 years, and a high rate of urban expansion emerged after 2003at the expense of the amount of other land use categories, espe-cially the paddy field. From the year 1988 to 2003, the impervioussurface area increased from 3% to 8%; however, it increased to 20%in 2009. On the other hand, the paddy field decreased substantiallyfrom 48% in 1988 to 40% in 2009. Water area changed slightly,while woodland and dry land decreased during the past 20 years.It should be noted that due to the policy of tree-planting, woodlandrepresented an increasing trend during 1994–2003.

3.2. Projected future land use scenarios

Land use scenarios of 2012 and 2018 were predicted with theassumption that the drivers of pre-2006 are still acting on the landuse, and no other policy arrests this trend. It must be emphasizedthat the Markov values do not represent realistic future states forthe basin. Rather, they are direct equivalents of land use changesthat occurred in a given time (Michael and John, 1994). The pre-dicted land use maps suggest continuing rapid increases of imper-vious surface from 23% to 31% with very high losses of paddy fieldduring 2012–2018 (Table 3). Impervious surface area will becomethe second main land use category and other categories representtrends of decline, confirming that urbanization is one of the mostimportant driving forces resulting in the general trends in landuse change in future.

Table 2Land use structures from 1988 to 2009(%).

Year Impervious surface Paddy field Water Woodland Dry land

1988 3 48 4 19 261994 5 47 4 17 272001 7 45 4 18 262003 8 44 4 18 262006 12 42 4 17 252009 20 40 3 15 22

Table 3Future land use scenarios predicted by the CA-Markov model (%).

Year Impervious surface Paddy field Water Woodland Dry land

2012 23 39 3 14 212018 31 34 3 13 19

Table 4The land use structures of each urbanization scenarios (%).

Year Impervious ratio Paddy field Water Woodland Dry land

1988 3 48 4 19 261994 6 46 4 19 252001 8 45 4 18 252003 9 45 4 18 252006 14 42 4 16 242009 20 39 4 15 222012 24 38 4 14 212018 31 33 3 13 19



3.3. Urbanization scenarios

The results of the urbanization scenarios are listed in Table 4. Itcan be seen that there are slight increases in impervious ratio foreach urbanization scenario compared to the corresponding landuse scenario and that the other land use categories correspond-ingly decline.

3.4. Calibration and validation of HEC-HMS for long term simulation

The R and E of the calibration period for daily runoff were 0.79and 0.78, respectively; the simulated mean annual runoff is389 mm with a relative error of �13.3%. The R and E of the valida-tion period (1998–2006) for daily runoff were 0.79 and 0.77,respectively; the simulated mean annual runoff is 460 mm witha relative error of 10.4%. The calibrated initial abstraction of all

sub-basins is 15 mm, and the other calibrated parameters of sub-basins and sub-reaches are shown in Tables 5 and 6. It can be seenfrom these tables that the values of the same parameter for sub-ba-sins and reaches change considerably, which is the result of auto-matic optimization. Comparison of observed and simulateddischarges of calibration and validation periods is shown in Figs.5 and 6.

These results show that the model performance was satisfac-tory during both calibration and validation periods, implying thatthe selected models from HEC-HMS were applicable to the QinhuaiRiver catchment for long term simulations.

3.5. Calibration and validation of HEC-HMS for flood events simulation

The calibrated parameter values of the sub-basins for floodevent simulation were the same as for long term simulation.

Table 5Calibrated subbasin parameters of long term simulation.

Subbasin Clark unit hydrograph parameters Baseflow parameters

Time of concentration (h) Storage coefficient (h) Recession constant Threshold ratio to peak

Sub1 1.03 1.03 0.90 1.00Sub2 1.03 1.03 0.95 0.88Sub3 1.00 1.00 0.10 0.01Sub4 1.03 1.03 0.90 0.01Sub5 0.10 0.10 0.10 0.01Sub6 1.00 1.00 0.10 0.01Sub7 1.03 1.03 0.90 0.01Sub8 1.00 1.00 0.10 0.01Sub9 1.00 1.00 0.10 0.01Sub10 1.03 1.03 0.90 0.60Sub11 1.03 1.03 0.90 0.88Sub12 1.03 1.03 0.10 0.01Sub13 0.50 0.10 0.95 0.01Sub14 0.50 0.10 0.10 0.01Sub15 0.50 0.10 0.10 0.01Sub16 0.10 1.03 0.10 0.01Sub17 1.03 1.03 0.95 0.99Sub18 1.03 1.03 0.10 0.01

Table 6Calibrated subreach parameters.

Reach Long term simulation Medium flood simulation Large flood simulation

Muskingum traveltime (h)

Muskingum weightingfactor





R1 100.0 0.30 100.5 0.29 102.0 0.50R2 3.0 0.30 2.9 0.45 4.4 0.29R3 150.0 0.30 100.0 0.20 101.5 0.20R4 150.0 0.30 100.0 0.29 101.3 0.29R5 5.0 0.10 4.9 0.07 4.9 0.03R6 50.0 0.10 33.3 0.07 33.8 0.06R7 0.1 0.40 0.1 0.50 0.1 0.50R8 1.0 0.10 1.0 0.15 1.0 0.05R9 5.0 0.10 4.9 0.15 4.9 0.05R10 6.5 0.01 6.4 0.02 11.0 0.01R11 20.0 0.20 13.3 0.13 30.1 0.13R12 1.0 0.01 1.0 0.02 1.4 0.02R13 10.0 0.01 9.8 0.02 22.2 0.01R14 25.0 0.10 16.7 0.15 10.9 0.07R15 10.0 0.30 9.8 0.45 9.8 0.29R16 90.0 0.15 60.0 0.10 60.8 0.10R17 1.0 0.10 1.0 0.15 1.0 0.07R18 150.0 0.20 150.0 0.20 44.5 0.13R19 1.0 0.30 1.0 0.45 1.3 0.29R20 30.0 0.01 20.0 0.01 20.1 0.01R21 0.1 0.30 0.1 0.29 0.1 0.28R22 5.0 0.30 4.9 0.20 4.7 0.06R23 40.0 0.30 39.2 0.45 39.5 0.19Average 37.2 0.19 29.6 0.21 26.6 0.16



However, the calibrated values of sub-reach parameters for floodevent were different to those of the long-term simulation; andparameter values for medium flood events were also different tothose for large flood events (Table 6). It is seen that the average val-ues of Muskingum travel time and weighting factor of each sub-reach for medium flood events are greater than those for largeflood events, which is reasonable because the travel time of largeflood events will be shorter and the weighting factor smaller. Thecalibration and validation results for flood events are listed in Table7. The comparison of observed and simulated discharges of eachflood event is shown in Fig. 7. It is seen that the simulated floodhydrographs demonstrate a good agreement with the observedhydrographs for most flood events, except flood number 199806.The relative error of simulated peak flow and flood volume was

below 20% for most events. The mean efficiency was 0.81, and in10 of the 16 flood hydrographs the efficiency was higher than0.8; the mean correlation coefficient was 0.89, and was greaterthan 0.8 in 15 of the 16 flood hydrographs. These results indicatethat the selected models from HEC-HMS were suitable for floodevent simulation in the catchment.

3.6. Impact of urbanization on mean annual runoff for 1986–2006

Long-term simulation was conducted to estimate the impact ofurbanization on runoff under the same meteorological conditionsas 1986–2006. HEC-HMS was run for 21 years without changingthe calibration parameters, for urbanization scenarios based onland use data of 1988, 1994, 2001, 2006, 2009, 2012, and 2018.

Table 8 summarizes the changes in mean annual runoff depthunder different urbanization scenarios. Mean annual runoff is pre-dicted to hardly change, with an increase of only 0.2% when theimpervious ratio increased from 3% to 31%, which was consistentwith the results of several studies in other regions (Choi and Deal,2008; Franczyk and Chang, 2009). Choi and Deal (2008) studiedland use change impact on the hydrology of the Kishwaukee Riverbasin (KRB) in the Midwestern USA and found that the land usescenarios result in small change in total runoff. Even under theUber scenario which is associated with very high populationgrowth, mean annual runoff has been predicted to increase by only1.7% by 2051. Franczyk and Chang (2009) predicted that a 8–15%expansion of urban land use throughout the Rock Creek basin(Portland), will only result in a 2.3–2.5% increase in annual runoffdepths, respectively. A possible explanation for such phenomena isthat when impervious area increases, the direct runoff increaseswhile the baseflow decreases, so that the total runoff would not in-crease considerably. Another reason might arise from using SCS-CNmethod for loss calculation; the original SCS-CN method is an infil-tration loss model for single storm that does not account for evap-oration and evapotranspiration, which might cause some errors inlong term simulation. The error caused by ignoring evaporation isexpected to increase as the impervious surface decreases.

3.7. Impact of urbanization on annual runoff for typical hydrologicalyears

An analysis was conducted between urbanization scenarios andannual rainfall amounts to determine how annual rainfall amountinteracts with urbanization effects on runoff. Three typical hydro-

0

500

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1500

20002200

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Rai

nfal

l (m

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ay)

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am f

low

(m

3 /sec

)

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Simulated Observed

1-Jan-1990 1-Jul-1990 1-Jan-1991 1-Jul-1991 31-Dec-1991

Fig. 5. Comparison of daily observed and simulated stream flow selected fromcalibration period.

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Simulated Observed

1-Jan-2003 1-Jul-2003 1-Jan-2004 1-Jul-2004 31-Dec-2004

Fig. 6. Comparison of daily observed and simulated stream flow selected fromvalidation period.

Table 7Summary of calibration and validation results for flood simulation at daily step.

FloodNo.

Observed peak flow(m3/s)

Simulated peakflow (m3/s)

Relative peak flowerror (%)

Observed floodvolume (mm)

Simulated floodvolume (mm)

Relative flood volumeerror (%)

R E

198706* 838 685 �18 228 221 �3 0.94 0.92198708 704 732 4 131 187 43 0.85 0.72198806* 376 370 �2 43 40 �7 0.82 0.79198906* 560 647 4 106 99 �7 0.95 0.95198908 764 808 6 110 126 15 0.92 0.90199106* 1280 1541 20 322 348 8 0.85 0.83199107 1262 1362 8 521 653 25 0.93 0.83199603 246 204 �17 24 21 �15 0.99 0.90199606 884 735 �17 173 210 21 0.93 0.72199806 583 532 �9 128 126 �2 0.63 0.46199906 630.3 460 �27 65 51 �22 0.97 0.79199907 878 754 �14 132 142 8 0.83 0.73200206 806 979 21 165 229 39 0.97 0.87200306 1106 1115 2 352 483 38 0.85 0.75200406 798 871 9 120 106 �11 0.89 0.89200607 595 556 �7 112 101 �10 0.90 0.81Average 0.89 0.81

* calibrated floods.



logical years (dry year with annual precipitation exceedence prob-ability of 90%, normal year with annual precipitation exceedenceprobability of 50%, and wet year with annual runoff exceedenceprobability of 10%) are selected, which are 1994, 2000 and 1991with annual precipitations of 695, 1055 and 1913 mm respectively.

Annual runoff depth increases very slightly with increasingimpervious surface area for all three typical hydrological years (Ta-ble 8). The runoff increase percentages for the dry year are a littlebit bigger than that for the wet year under the same urbanizationscenarios; even when impervious ratio reaches 31%, the annualrunoff increased 5.6% in the dry year. Considering the model uncer-tainty and that the largest increase in annual runoff was 13 mmcomparing with annual runoff 1384 mm at the baseline year,urbanization has little effect on annual runoff, as explained at theend of Section 3.6.

3.8. The impact of urbanization on flood events

The calibrated HEC-HMS model was applied to each of theurbanization scenarios to assess the effects of urbanization onflood events in the watershed. Eight flood events with differentmagnitude peak discharges were selected to assess the potentialchange in response to urbanization. The simulation results are pre-sented in Tables 9 and 10, where it can be seen that (1) urbandevelopments affect peak flows and runoff volumes more thanlong-term runoff, and (2) the flood volumes increased slightlymore than that of flood peaks for the same increase of impervioussurface ratio. These results agreed with those from Dreher andPrice (1997), Im et al. (2003) and Hejazi and Markus (2009). Thelarger percentage increase in flood volume than that in flood peakwould increase the duration of flood inundation.

0 4 8 12 16 20 24 28 320

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0 4 8 12 16 0 4 8 12 16 20 24 0 4 8 12 16 20 24 28 32 0 4 8 12

0 4 8 12 16 20 24 0 4 8 12 16 0 4 8 12 0 4 8 12 16

0 4 8 12 16 20 24 0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 0 4 8 12 160

150

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Storm 198706

Stre

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Storm 198708

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Storm 198906

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ream

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Storm 200406 Storm 200607

SimulatedSt

ream

flo

w (

m3 /s

)

Time (day) Observed

Fig. 7. Comparison of observed and simulated stream flow of 16 flood events.

Table 8Simulated annual runoff under different urbanization scenarios.

Urbanizationscenarios

Imperviousratio (%)

Long term Wet year Normal year Dry year

Simulatedannual runoff(mm)

Increasedfrom1988(%)







1988 3 431 1384 261 901994 6 431 0.0 1384 0.0 261 0.2 91 1.12001 8 431 0.0 1386 0.1 262 0.5 91 1.12003 9 431 0.0 1387 0.2 263 0.6 92 2.22006 14 432 0.2 1389 0.4 264 1.2 93 3.32009 20 432 0.2 1392 0.6 265 1.7 94 4.42012 24 432 0.2 1394 0.7 266 2.0 94 4.42018 31 432 0.2 1397 0.9 268 2.6 95 5.6



The results in Tables 9 and 10 also show that daily flood peakdischarges and flood volumes of small flood events increased dueto urbanization by a larger proportion than did those of large floodevents, which means that small floods are more sensitive to urban-ization than large floods. This finding agrees well with the litera-ture which reports that flood magnitudes of rare events are lesssensitive to increases in watershed impervious surface cover thanthose with shorter recurrence intervals (Hollis, 1975; Booth,1988; Konrad, 2003). Such phenomena were explained by Beighleyet al. (2003), who noted that for smaller events, near the thresholdof runoff, increased imperviousness resulted in significantly morerunoff. For larger storms, the effect of increased imperviousnesswas minimal because a larger fraction of the watershed ‘‘saturates’’relatively early during the event, essentially diminishing the ef-fects of initial storage capacity provided by non-urban lands. Fora given increase in impervious area, the percent increase in peakdischarge and runoff volume generally decreases with increasingrainfall magnitude. However, Sheng and Wilson (2009) found thatfor small watersheds (with areas ranging from 4.7 to 229.7 km2)both the frequent and rare floods were sensitive to urbanization.This is because basin size influences hydrological sensitivity to ur-ban development, and smaller basins experience relatively greaterimpacts than larger ones. It should also be noted that the relativeincrease of flood peak and flood volume depends not only on therelative increase of impervious surface, but also on the degree ofurbanization and geographic region.

3.9. Sensitivity of flood changes to increasing impervious surface

The sensitivity of peak discharge and flood volume to increasingurbanization (impervious surface) was also examined. Fig. 8 showsthe simulated daily peak discharge and flood volume with increas-ing impervious surface for various event magnitudes. All the curvesare close to linear, and the curve slopes of small floods are steeperthan those of large floods, which again means that small floods aremore sensitive to urbanization than are large floods. These results

are in agreement with those from Changnon et al. (1996), Bhaduriet al. (2001) and Choi and Deal (2008), but not with that from Brun

Table 9Peak flow response to urbanization.

Scenarios Impervious ratio 198706 200406 19910607 200306 198906 200607 199603 198806 Average (%)

Qp 4 Qp 4 Qp 4 Qp 4 Qp 4 Qp 4 Qp 4 Qp 4

1988 3 685 867 1541 1111 647 551 202 3701994 6 687 0.3 869 0.2 1543 0.1 1113 0.2 649 0.3 551 0.0 203 0.5 372 0.5 0.32001 8 691 0.9 872 0.6 1546 0.3 1117 0.5 653 0.9 554 0.5 206 2.0 376 1.6 0.92003 9 693 1.2 874 0.8 1547 0.4 1119 0.7 656 1.4 554 0.5 207 2.5 377 1.9 1.22006 14 700 2.2 879 1.4 1555 0.9 1124 1.2 663 2.5 557 1.1 212 5.0 383 3.5 2.42009 20 709 3.5 886 2.2 1562 1.4 1133 2.0 672 3.9 561 1.8 218 7.9 390 5.4 3.52012 24 716 4.5 891 2.8 1567 1.7 1139 2.5 679 4.9 563 2.2 223 10.4 396 7.0 4.52018 31 726 6.0 898 3.6 1576 2.3 1147 3.2 688 6.3 567 2.9 230 13.9 405 9.5 6.0

Qp = Simulated peak flow (m3/s); 4 = increased from1988 (%).

Table 10Flood volume response to urbanization.

Scenarios Impervious ratio 198706 200406 19910607 200306 198906 200607 199603 198806 Average(%)

Vp 4 Vp 4 Vp 4 Vp 4 Vp 4 Vp 4 Vp 4 Vp 4

1988 3 221 105 348 481 99 100 20 401994 6 222 0.5 106 1.0 349 0.3 482 0.2 100 1.0 100 0.0 20 0.0 40 0.0 0.42001 8 224 1.4 107 1.9 351 0.9 485 0.8 100 1.0 101 1.0 21 5.0 41 2.5 1.82003 9 224 1.4 107 1.9 351 0.9 485 0.8 101 2.0 101 1.0 21 5.0 41 2.5 1.92006 14 226 2.3 109 3.8 353 1.4 488 1.5 102 3.0 101 1.0 22 10.0 42 5.0 3.52009 20 229 4.1 111 5.7 357 2.6 492 2.3 103 4.0 102 2.0 23 15.0 43 7.5 5.42012 24 231 4.5 112 6.7 359 3.2 496 3.1 105 6.1 103 3.0 24 20.0 44 10.0 7.12018 31 234 5.9 114 8.6 363 4.3 499 3.7 106 7.1 103 3.0 24 20.0 46 15.0 8.5

Vp = Simulated flood volume (mm); 4 = increased from 1988 (%).

0 5 10 15 20 25 30 350

3

6

9

12

15

Flood 199603

Linear Fit of flood 199603 Flood 199106

Linear Fit of flood 199106

Flood 198706


Peak

flo

w in

crea

se (

%)

Impervious ratio (%)

0 5 10 15 20 25 30 350

3

6

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Flood 199603


Flood 198706


Flood 199106


Floo

d vo

lum

e in

crea

se (

%)

Impervious ratio (%)

Fig. 8. The potential changes in peak flow and flood volume with increasingimpervious ratio for varied amplitudes of floods.



and Band (2000) and Wissmar et al. (2004). In the study of Brunand Band (2000), a logistic relationship between runoff ratio andimperviousness, and an exponential relationship between baseflow and imperviousness was found when imperviousness was in-creased up to 90%. Wissmar et al. (2004) found that the magnitudeof flood flows for urban watersheds in the lower Cedar River drain-age in the US tends to increase nonlinearly when impervious ratiosreach 43–74% levels. In the present study, the percentage of urbanland use is not high enough to result in nonlinear changes in flows.

4. Summary and conclusion

This paper has attempted to connect a distributed hydrologicalmodel and a dynamic land use change model as a tool for examin-ing urbanization influences on annual runoff and flood of theQinhuai River watershed in Jiangsu Province, China. The hydrolog-ical model based on Hydrologic Engineering Center’s HydrologicModeling System (HEC-HMS) was calibrated and validated, andrepeatedly run with various urbanization scenarios. The urbaniza-tion scenarios were developed based on historical land use mapsobtained from TM images and CBERS image, and future land usemaps were generated by an integrated Markov Chain and CellularAutomata model (CA-Markov model). The following conclusionsare drawn from the study.

Firstly, there were slight increases in mean annual runoff of thewhole watershed as a response to urbanization, which implies thatthe region is not likely to undergo significant changes in the avail-ability of surface water resource due to future urban growthpressures.

Secondly, the changes of annual runoff in dry years are propor-tionally greater than in wet years, which means that availability ofsurface water resource in dry years is more sensitive to urbanization.

Thirdly, the daily flood peaks flow and flood volumes increasewith imperviousness for all flood events; daily peak flows increaseless than that of flood volume in all flood events due to urbaniza-tion, daily peak flow discharges and flood volumes of small floodsincreased proportionally more than those of large floods with thesame urbanization scenario, implying that small floods and floodvolumes would be more sensitive to urbanization.

Fourthly, the potential changes in peak discharge and flood vol-ume with increasing impervious surface showed linear relation-ships, and the curve slopes of small floods are steeper than thoseof large floods. The possible reason for this linear relationship isthat the proportion of urban land use is not high enough to resultin nonlinear changes in flows.

It is worth noting that the CA-Markov model was used underthe assumption that the land management policy will remain thesame and that the hydrologic response of each hydrologic soil typeis constant during the entire study period. In reality, the land man-agement policy should change, with newly built areas constructedusing low impact drainage design, which can mitigate the hydro-logic impacts of urbanisation (Meierdiercks et al., 2010; Ogdenet al., 2011). Therefore, the changing land management policy,hydrologic soil type and drainage networks will be considered inour further studies.

Nevertheless, a framework is proposed in this study which iscomposed of three segments: projecting future land use using adistributed land use change model, developing urbanization sce-narios by overlaying a series of impervious surfaces to a baselineland use map, and assessing hydrologic response of urbanizationwith a distributed hydrological model. Our study demonstratesthat this is a good approach to evaluate the hydrologic impactsof urbanization, which must be considered in watershed manage-ment, water resources planning, and flood planning for sustainabledevelopment.

Acknowledgement

This work was supported by the National Natural ScienceFoundation of China (No. 40730635) and the Priority AcademicProgram Development of Jiangsu Higher Education Institutions.The corresponding author was also supported by the Programmeof Introducing Talents of Discipline to Universities—the 111 Projectof Hohai University. The authors would like to express their greatthanks for the reviewers’ comments and suggestions which havegreatly improved the quality of the paper. Special thanks are givento Prof. Tim Fletcher who kindly corrected the language and pro-vided valuable comments and advice that greatly improved thequality of the paper.

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