-
an
Jinkang Du a, Li Qian a, Hanyi Rui a b a a c,a School of
Geographic and Oceanographic Sciences, Nanb Earthquake
Administration of Guangxi Antonomous RecDepartment of Geosciences,
University of Oslo, P.O. Box
a r t i c l e i n f o
Article history:Received 22 July 2011Received in revised form 7
June 2012
150 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
oods(Yang et al., 2012), which are essential for planning and
operationof civil water resource projects, and for early ood
warning. Theinuence of urbanization as one of the important land
use and land
processes within watersheds by altering surface inltration
char-acteristics. The expected results of urbanization include
reducinginltration, baseow, lag times, increasing storm ow
volumes,peak discharge, frequency of oods, 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 rainfallrunoff,
and
Corresponding author. Tel.: +47 22 855825; fax: +47 22
854215.
Journal of Hydrology 464465 (2012) 127139
Contents lists available at
H
.e lsE-mail address: [email protected] (C.-Y. Xu).surface
showed a linear relationship, and the changes of small oods were
larger than those of largeoods with the same impervious increase,
indicating that the small oods were more sensitive than largeoods
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 ood management forsustainable
development.
2012 Elsevier B.V. All rights reserved.
1. Introduction
The world population has grown very rapidly over the last
cover changes on runoff and oods within watersheds is one of
themain research topics in the past decades.
It is widely recognized that urbanization changes
hydrologicalAccepted 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 owFlood volume0022-1694/$ - see front matter 2012
Elsevier B.V. Ahttp://dx.doi.org/10.1016/j.jhydrol.2012.06.057,
Tianhui Zuo , Dapeng Zheng , Youpeng Xu , C.-Y. Xujing University,
Nanjing 210093, Chinagion, Nanning 530022, China1047 Blindern,
NO-0316 Oslo, Norway
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 ood
eventsof the Qinhuai River watershed in Jiangsu Province, China.
The Hydrologic Engineering CentersHydrologic 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 streamow 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 ChinaBrazil 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 ow, and oodvolume
have increased to different degrees due to urban expansion during
the study period (19882009),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 oodswould increase from 2.3% to 13.9%. The change trend of
ood volumes is similar with that of peak dis-charge, but with
larger percentage changes than that of daily peak ows in all
scenarios. Sensitivity anal-ysis revealed that the potential
changes in peak discharge and ood volume with increasing
imperviousan integrated hydrological modeling system for Qinhuai
River basin, China
Assessing the effects of urbanization on
Journal of
journal homepage: wwwll rights reserved.nual runoff and ood
events using
SciVerse ScienceDirect
ydrology
evier .com/ locate / jhydrol
-
2. Materials and methods
logyexamined the variation of each parameter for different
levels ofurbanization. Bhaskar (1988) adopted Clarks instantaneous
unithydrograph concept to determine the parameters that inuencethe
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 ows, low ows and peak ows. 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 dene 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 Centers 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 (bifurcationa split in the ow 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 empiricalstatistical 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 the
128 J. Du et al. / Journal of Hydroother 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 of2.1. Study area and data
Qinhuai River basin is located between 118390 and
119190Elongitude and 31340 to 32100N 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 eld, woodland, impervious sur-face,
water, and dry land. Among those, paddy eld and dry landare the
main land use types (for details see Section 3.1). The mainsoil
types are yellowbrown soil, purple soil, limestone soil, paddysoil,
and gray uvo-aquic soil.
Seven raingage stations and two stream ow gauging stations atthe
outlets of the basin were used for the study. The
watershedlocation, elevation, distribution of rainfall and ow
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 (19862006) from the China
Meteorologicalboth 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-Markovmodel andHEC-HMSmodel systemwere
used as an integrated system to quantify the annual runoffand ood
response to urbanization. Themain objective of this studywas to
develop and test the integrated modeling system for analyz-ing the
effects of sub-urban development on runoff and ood 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 ood 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 ood 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-HMSmodeling sys-tem for simulating
daily stream ow 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.
464465 (2012) 127139Data 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
-
iver
ology1:75,000 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 ChinaBrazil 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
datasets
Fig. 1. Map of Qinhuai R
J. Du et al. / Journal of Hydrare 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 coefcients
forradiometric calibration, geometric rectication and projected
tothe Universal Transverse Mercator (UTM) ground coordinates witha
spatial resampling of 30 m. Geometric rectication 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 classication method with maximum
likelihoodclustering and DEM data were employed for image
classication asa hybrid method to generate land use maps and
post-classicationanalysis was applied to create the trend map of
land use changes.Land use categories were paddy eld, dry land,
woodland, impervi-ous surface and water. Pure pixels, rather than
mixed pixels, wereselected as training samples. Mixed classes such
as paddy eld andwoodland were separated with the aid of DEM data.
Ground tru-thing was performed to assist in the imagery
classication and tovalidate the nal results. Each image was
classied following thesame method.
Overall accuracy and Kappa value were selected as
evaluationcriteria for the classication. An error matrix was
generated basedon test samples for each land use map. The columns
of error ma-trix represent the reference data by ground truthing,
while therows indicate the classied 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 NPr
i1xii Pr
i1xi xiN2 Pri1xi xi
1
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 and
basin used in this study.
464465 (2012) 127139 129column 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 classication results and predicted results),
val-ues for the overall accuracy and Kap equal to 1.
In this study, the overall classication 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 nite 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, St1, and not on the sequence of valuesSt2, St3, . .
.,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 Pijrepresenting the
probabilities of each land use category changingto every other
category (or remaining the same) during that per-iod. Pij equation
is as follows:
-
logy130 J. Du et al. / Journal of HydroPij
P11 P12 P1nP21 P22 P2n... ..
. . .. ..
.
Pn1 Pn2 Pnn
266664
3777750 6 Pij < 1 and
XN
i1;j1Pij 1 i; j 1;2 n
2
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-dened transition rules. Five components were included:
(a)a space composed of discrete cells, (b) a nite set of possible
statesassociated to every cell, (c) a neighborhood of adjacent
cells whosestate inuences 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 20032006. 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
Fig. 2. The land use m464465 (2012) 127139using 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
le that records the probability of eachland use category changing
to every other category. The transitionareas matrix is a text le
that records the number of pixels that areexpected to change from
each land use type to other land use typeover the specied number of
time units. The conditional probabil-ity images report the
probability of each land cover type to befound at each pixel after
the specied 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 0255.
Theprobability maps created by the Markov module were used asthe
suitability map.
Third, a 5 5 contiguity lter was used to generate a spatial
ex-plicit contiguity-weighting factor to change the state of cells
basedon its neighbors. The lter emphasized that the spatial scale
of150 m 150 m around a cell would have more signicant 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 classication of CBERS image from 2009 (Fig. 2d) to testthe
model accuracy according to the area of each land use category.The
classication 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.
aps 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: rst,
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
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 ow, 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-
J. Du et al. / Journal of Hydrology 464465 (2012) 127139 131Soil
data of the study area were generated from existing SoilSurvey maps
at a scale of 1:75,000. Soil maps were rectied 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 yellowbrown soil, purple soil, limestonesoil,
paddy soil, and gray uvo-aquic soil. According to the rulesof
hydrologic soil group classications developed by the US
NaturalResource Conservation Service (NRCS), only hydrologic soil
groupsB (paddy soil, purple soil) and C (yellowbrown soil,
limestone soiland gray uvo-aquic soil) are presented in the basin
(Fig. 3), indi-cating a moderate inltration rate and a slow
inltration 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 ofFig.
3. Hydrologic soil map of the basin.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 UsersManual (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 P Ia2
P Ia S 3
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 P 0:2S2
P 0:8S 4
The maximum retention (S) is determined using the following
equa-tion (SI system):
S 25;400 254CNCN
5
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. Clarks 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
storagecoefcient. The time-area relationship can be represented by
asmooth function requiring only one parameter, the time of
concen-tration. The storage coefcient is an index of the temporary
storage
Table 1Curve number for hydrologic soil groups B and C.
Land use B C
Paddy eld 76 84Woodland 64 73Impervious surface 98 98
Water 95 95Dry land 76 82
-
logy132 J. Du et al. / Journal of Hydroof precipitation excess
in the watershed as it drains to the outletpoint. The two
parameters can be estimated via calibration ifgauged precipitation
and streamow data are available or by equa-tions presented in
Bedient and Huber (1992).
In HEC-HMS, the baseow model is applied both at the start
ofsimulation of a storm event, and later in the event as the
delayedsubsurface ow reaches the watershed channels. The
recessionmodel adopted in present study explains the drainage from
naturalstorage in a watershed. It denes the relationship of the
baseowQt at any time t to an initial value Q0 as:
Qt Q0Kt 6
where K is an exponential decay constant. A threshold ow,
afterthe peak of the direct runoff, should be specied either as a
owrate or as a ratio to the computed peak ow when applying
reces-sion model (Jha and Mahana, 2010).
The Muskingum method was adopted to compute outow fromeach
reach. The method uses the following equation:
Fig. 4. Sketch map of hydrologi464465 (2012) 127139Q2 c1 c2I1 1
c1Q1 c2I2c1 2 Dt2 K 1 X Dtc2 Dt 2 K X2 K 1 X Dt
7
where I1, I2 are the inows to the routing reach at the beginning
andend of computation interval respectively, Q1 and Q2 are the
outowsfrom 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
c elements in Basin Model.
-
ues to each grid (30 m 30 m resolution) with the help of
HEC-
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 19992003
simula-tion using land use data of 2001 and rainfall data of
19992003,and for 20042006 simulation using land use data of 2006
andrainfall data of 20042006.
In order to assess the urbanization effects on ood ow, four-teen
ood events with daily peak discharge greater than 500 m3/s and two
other smaller ood events during 19862006 were se-
ology 464465 (2012) 127139 133GeoHMS 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
approximately6498 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 streamow data
collected atthe outlets of the watershed. The objective of the
model calibrationwas to match simulated daily runoff with the
observed data withwell 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-
Table 2Land use structures from 1988 to 2009(%).
Year Impervious surface Paddy eld 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 eld Water Woodland Dry land
2012 23 39 3 14 212018 31 34 3 13 19
J. Du et al. / Journal of Hydrdifferent meteorological
conditions and land cover conditions.In this study, two evaluation
criteria, correlation coefcient (R)
and model efciency (E) (Nash and Sutcliffe, 1970) were used to
as-sess model performance. To calibrate and verify the
HEC-HMSmodel, 21-year (19862006) streamow and precipitation
datawere used for the study watershed. The observed runoff
datasetwas divided into a calibration period (19861998) and a
verica-tion period (19992006) based on the land use data years
1988,1994, 2001, and 2006. For model calibration, land use data
for1988 and rainfall data for 19861992 were used for
19861992simulation, and land use data for 1994 and rainfall data
for19931998 were used for 19931998 simulation.
Initial abstraction, time of concentration, storage
coefcient,recession constant, baseow 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 efciency
(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
the basin. Rather, they are direct equivalents of land use
changes
that 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
eldduring 20122018 (Table 3). Impervious surface area will
becomethe second main land use category and other categories
representtrends of decline, conrming that urbanization is one of
the mostimportant driving forces resulting in the general trends in
landuse change in future.
Table 4The land use structures of each urbanization scenarios
(%).
Year Impervious ratio Paddy eld 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 22lected for calibration and
validation. Four ood events with differ-ent peak discharges were
selected for model calibration. Thecalibration parameters for ood
events simulation were same asthose for long-term simulation. The
optimized parameter sets foreach calibrated ood 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 19882009, paddy eld 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 eld.
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 eld 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 19942003.
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 for2012 24 38 4 14 212018 31 33 3 13 19
-
3.3. Urbanization scenarios
The R and E of the calibration period for daily runoff were
0.79
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
seen
These results show that the model performance was satisfac-
Table 5Calibrated subbasin parameters of long term
simulation.
Subbasin Clark unit hydrograph parameters Baseow parameters
Time of concentration (h) Storage coefcient (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
134 J. Du et al. / Journal of Hydrology 464465 (2012) 127139and
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
(19982006) 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
Table 6Calibrated subreach parameters.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
simulationReach Long term simulation Medium ood simulat
Muskingum traveltime (h)
Muskingum weightingfactor
Muskingum traveltime (h)
R1 100.0 0.30 100.5R2 3.0 0.30 2.9R3 150.0 0.30 100.0R4 150.0
0.30 100.0R5 5.0 0.10 4.9R6 50.0 0.10 33.3R7 0.1 0.40 0.1R8 1.0
0.10 1.0R9 5.0 0.10 4.9R10 6.5 0.01 6.4R11 20.0 0.20 13.3R12 1.0
0.01 1.0R13 10.0 0.01 9.8R14 25.0 0.10 16.7R15 10.0 0.30 9.8R16
90.0 0.15 60.0R17 1.0 0.10 1.0R18 150.0 0.20 150.0R19 1.0 0.30
1.0R20 30.0 0.01 20.0R21 0.1 0.30 0.1R22 5.0 0.30 4.9R23 40.0 0.30
39.2Average 37.2 0.19 29.6tory 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 ood events
simulation
The calibrated parameter values of the sub-basins for oodevent
simulation were the same as for long term simulation.from 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.ion
Large ood simulation
Muskingum weightingfactor
Muskingum traveltime (h)
Muskingum weightingfactor
0.29 102.0 0.500.45 4.4 0.290.20 101.5 0.200.29 101.3 0.290.07
4.9 0.030.07 33.8 0.060.50 0.1 0.500.15 1.0 0.050.15 4.9 0.050.02
11.0 0.010.13 30.1 0.130.02 1.4 0.020.02 22.2 0.010.15 10.9
0.070.45 9.8 0.290.10 60.8 0.100.15 1.0 0.070.20 44.5 0.130.45 1.3
0.290.01 20.1 0.010.29 0.1 0.280.20 4.7 0.060.45 39.5 0.190.21 26.6
0.16
-
below 20% for most events. The mean efciency was 0.81, and in10
of the 16 ood hydrographs the efciency was higher than0.8; the mean
correlation coefcient was 0.89, and was greaterthan 0.8 in 15 of
the 16 ood hydrographs. These results indicatethat the selected
models from HEC-HMS were suitable for oodevent simulation in the
catchment.
3.6. Impact of urbanization on mean annual runoff for
19862006
Long-term simulation was conducted to estimate the impact
ofurbanization on runoff under the same meteorological conditionsas
19862006. 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 consistent
0
500
1000
1500
20002200
800
600
400
200
0
Rainfall
Rai
nfal
l (mm
/day)
Stre
am flo
w (m
3 /sec
)
Date
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 ow
selected fromcalibration period.
1200
1600 0
ay)
3 /sec
)
Simulated Observed
J. Du et al. / Journal of Hydrology 464465 (2012) 127139
135However, the calibrated values of sub-reach parameters for
oodevent were different to those of the long-term simulation;
andparameter values for medium ood events were also different
to
0
400
800
600
400
200 Rainfall
Rai
nfal
l (mm
/d
Stre
am fl
ow (m
Date1-Jan-2003 1-Jul-2003 1-Jan-2004 1-Jul-2004 31-Dec-2004
Fig. 6. Comparison of daily observed and simulated stream ow
selected fromvalidation period.those for large ood events (Table
6). It is seen that the average val-ues of Muskingum travel time
and weighting factor of each sub-reach for medium ood events are
greater than those for largeood events, which is reasonable because
the travel time of largeood events will be shorter and the
weighting factor smaller. Thecalibration and validation results for
ood events are listed in Table7. The comparison of observed and
simulated discharges of eachood event is shown in Fig. 7. It is
seen that the simulated oodhydrographs demonstrate a good agreement
with the observedhydrographs for most ood events, except ood number
199806.The relative error of simulated peak ow and ood volume
was
Table 7Summary of calibration and validation results for ood
simulation at daily step.
FloodNo.
Observed peak ow(m3/s)
Simulated peakow (m3/s)
Relative peak owerror (%)
Observolum
198706* 838 685 18 228198708 704 732 4 131198806* 376 370 2
43198906* 560 647 4 106198908 764 808 6 110199106* 1280 1541 20
322199107 1262 1362 8 521199603 246 204 17 24199606 884 735 17
173199806 583 532 9 128199906 630.3 460 27 65199907 878 754 14
132200206 806 979 21 165200306 1106 1115 2 352200406 798 871 9
120200607 595 556 7 112Average
* calibrated oods.with 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
815%expansion of urban land use throughout the Rock Creek
basin(Portland), will only result in a 2.32.5% increase in annual
runoffdepths, respectively. A possible explanation for such
phenomena isthat when impervious area increases, the direct runoff
increaseswhile the baseow 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
inl-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-
ved oode (mm)
Simulated oodvolume (mm)
Relative ood volumeerror (%)
R E
221 3 0.94 0.92187 43 0.85 0.7240 7 0.82 0.7999 7 0.95 0.95
126 15 0.92 0.90348 8 0.85 0.83653 25 0.93 0.8321 15 0.99
0.90
210 21 0.93 0.72126 2 0.63 0.4651 22 0.97 0.79
142 8 0.83 0.73229 39 0.97 0.87483 38 0.85 0.75106 11 0.89
0.89101 10 0.90 0.810.89 0.81
-
logy0 4 8 12 16 20 24 28 320
200400600800
1000
0 4 8 12 16 20 240
200
400
600
800
200400600800
1000
400
800
1200
1600
Stre
am fl
ow (m
3 /s)
Time (day)
Storm 198706
Stre
am fl
ow (m
3 /s)
Time (day)
Storm 198708
am fl
ow (m
3 /s)
Storm 198908
am fl
ow (m
3 /s)
Storm 199106
136 J. Du et al. / Journal of Hydrological 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.
0 0
0
250
500
750
1000
0
150
300
450
600
0200400600800
1000
0
300
600
900
1200
0 4 8 12 16 0 4 8 12 16 20 24
0 4 8 12 16 20 24 0 4 8 12 16
0 4 8 12 16 20 24 0 4 8 12 16 20 24 28 32 36
Stre
Time (day)
Stre
Time (day)
Stre
am fl
ow (m
3 /s)
Time (day)
Storm 199606
Stre
am fl
ow (m
3 /s)
Time (day)
Storm 199806
Stre
am fl
ow (m
3 /s)
Time (day)
Storm 200206
Stre
am fl
ow (m
3 /s)
Time (day)
Storm 200306
Simulated
Fig. 7. Comparison of observed and simu
Table 8Simulated annual runoff under different urbanization
scenarios.
Urbanizationscenarios
Imperviousratio (%)
Long term Wet year
Simulatedannual runoff(mm)
Increasedfrom1988(%)
Simulatedannual runoff(mm)
If(
1988 3 431 13841994 6 431 0.0 1384 02001 8 431 0.0 1386 02003 9
431 0.0 1387 02006 14 432 0.2 1389 02009 20 432 0.2 1392 02012 24
432 0.2 1394 02018 31 432 0.2 1397 00
100
200
300
400
0
200
400
600
800
300600900
12001500
50100150200250
0 4 8 12 16 20 0 4 8 12 16 20 24Stre
am fl
ow (m
3 /s)
Time (day)
Storm 198806
Stre
am fl
ow (m
3 /s)
Time (day)
Storm 198906
am fl
ow (m
3 /s)
Storm 199107
am fl
ow (m
3 /s)
Storm 199603
464465 (2012) 1271393.8. The impact of urbanization on ood
events
The calibrated HEC-HMS model was applied to each of
theurbanization scenarios to assess the effects of urbanization
onood events in the watershed. Eight ood 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 ows and runoff volumes more
thanlong-term runoff, and (2) the ood volumes increased
slightlymore than that of ood 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 ood volume than that in
ood peakwould increase the duration of ood inundation.
0 0
0140280420560700
0200400600800
1000
0200400600800
1000
0 4 8 12 16 20 24 28 32 0 4 8 12
0 4 8 12 0 4 8 12 16
0 4 8 12 16 20 0 4 8 12 160
150
300
450
600
Stre
Time (day)
Stre
Time (day)
Stre
am fl
ow (m
3 /s)
Time (day)
Storm 199906
Stre
am fl
ow (m
3 /s)
Time (day)
Storm 199907
Stre
am fl
ow (m
3 /s)
Time (day)
Storm 200406 Storm 200607
Stre
am fl
ow (m
3 /s)
Time (day) Observed
lated stream ow of 16 ood events.
Normal year Dry year
ncreasedrom1988%)
Simulatedannual runoff(mm)
Increasedfrom1988(%)
Simulatedannual runoff(mm)
Increasedfrom1988(%)
261 90.0 261 0.2 91 1.1.1 262 0.5 91 1.1.2 263 0.6 92 2.2.4 264
1.2 93 3.3.6 265 1.7 94 4.4.7 266 2.0 94 4.4.9 268 2.6 95 5.6
-
0306 198906 200607 199603 198806 Average (%)
4 Qp 4 Qp 4 Qp 4 Qp 411 647 551 202 37013 0.2 649 0.3 551 0.0
203 0.5 372 0.5 0.3
2001 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 1.2
24 1.2 663 2.5 557 1.1 212 5.0 383 3.5 2.433 2.0 672 3.9 561 1.8
218 7.9 390 5.4 3.539 2.5 679 4.9 563 2.2 223 10.4 396 7.0 4.547
3.2 688 6.3 567 2.9 230 13.9 405 9.5 6.0
00306 198906 200607 199603 198806 Average(%)
p 4 Vp 4 Vp 4 Vp 4 Vp 481 99 100 20 4082 0.2 100 1.0 100 0.0 20
0.0 40 0.0 0.485 0.8 100 1.0 101 1.0 21 5.0 41 2.5 1.885 0.8 101
2.0 101 1.0 21 5.0 41 2.5 1.988 1.5 102 3.0 101 1.0 22 10.0 42 5.0
3.592 2.3 103 4.0 102 2.0 23 15.0 43 7.5 5.496 3.1 105 6.1 103 3.0
24 20.0 44 10.0 7.199 3.7 106 7.1 103 3.0 24 20.0 46 15.0 8.5
12
15
Flood 199603
Linear Fit of flood 199603 Flood 199106 Flood 198706
e (%
)
ologyThe results in Tables 9 and 10 also show that daily ood
peakdischarges and ood volumes of small ood events increased dueto
urbanization by a larger proportion than did those of large
oodevents, which means that small oods are more sensitive to
urban-ization than large oods. This nding agrees well with the
litera-
2006 14 700 2.2 879 1.4 1555 0.9 112009 20 709 3.5 886 2.2 1562
1.4 112012 24 716 4.5 891 2.8 1567 1.7 112018 31 726 6.0 898 3.6
1576 2.3 11
Qp = Simulated peak ow (m3/s); 4 = increased from1988 (%).
Table 10Flood volume response to urbanization.
Scenarios Impervious ratio 198706 200406 19910607 2
Vp 4 Vp 4 Vp 4 V1988 3 221 105 348 41994 6 222 0.5 106 1.0 349
0.3 42001 8 224 1.4 107 1.9 351 0.9 42003 9 224 1.4 107 1.9 351 0.9
42006 14 226 2.3 109 3.8 353 1.4 42009 20 229 4.1 111 5.7 357 2.6
42012 24 231 4.5 112 6.7 359 3.2 42018 31 234 5.9 114 8.6 363 4.3
4
Vp = Simulated ood volume (mm); 4 = increased from 1988
(%).Table 9Peak ow response to urbanization.
Scenarios Impervious ratio 198706 200406 19910607 20
Qp 4 Qp 4 Qp 4 Qp1988 3 685 867 1541 111994 6 687 0.3 869 0.2
1543 0.1 11
J. Du et al. / Journal of Hydrture which reports that ood
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 signicantly morerunoff. For larger
storms, the effect of increased imperviousnesswas minimal because a
larger fraction of the watershed saturatesrelatively 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 oods were sensitive to urbanization.This is because basin size
inuences hydrological sensitivity to ur-ban development, and
smaller basins experience relatively greaterimpacts than larger
ones. It should also be noted that the relativeincrease of ood peak
and ood volume depends not only on therelative increase of
impervious surface, but also on the degree ofurbanization and
geographic region.
3.9. Sensitivity of ood changes to increasing impervious
surface
The sensitivity of peak discharge and ood volume to
increasingurbanization (impervious surface) was also examined. Fig.
8 showsthe simulated daily peak discharge and ood volume with
increas-ing impervious surface for various event magnitudes. All
the curvesare close to linear, and the curve slopes of small oods
are steeperthan those of large oods, which again means that small
oods aremore sensitive to urbanization than are large oods. These
results464465 (2012) 127139 137are in agreement with those from
Changnon et al. (1996), Bhaduriet al. (2001) and Choi and Deal
(2008), but not with that from Brun
0 5 10 15 20 25 30 350
3
6
9 Linear Fit of flood 199106 Linear Fit of flood 198706
Peak
flow
incr
eas
Impervious ratio (%)
0 5 10 15 20 25 30 350
3
6
9
12
15
18
21 Flood 199603
Linear Fit of flood 199603
Flood 198706
Linear Fit of flood 198706
Flood 199106
Linear Fit of flood 199106
Floo
d vo
lum
e in
crea
se (%
)
Impervious ratio (%)Fig. 8. The potential changes in peak ow and
ood volume with increasingimpervious ratio for varied amplitudes of
oods.
-
logic impacts of urbanisation (Meierdiercks et al., 2010;
Ogdenet al., 2011). Therefore, the changing land management
policy,
Hydrol. Eng. 12, 3341.
logyhydrologic 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
impactsand 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 baseow
and imperviousness was found when imperviousness was in-creased up
to 90%. Wissmar et al. (2004) found that the magnitudeof ood ows
for urban watersheds in the lower Cedar River drain-age in the US
tends to increase nonlinearly when impervious ratiosreach 4374%
levels. In the present study, the percentage of urbanland use is
not high enough to result in nonlinear changes in ows.
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 inuences on annual runoff and ood of
theQinhuai River watershed in Jiangsu Province, China. The
hydrolog-ical model based on Hydrologic Engineering Centers
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 signicant 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 ofsurfacewater resource indryyears ismore sensitive
tourbanization.
Thirdly, the daily ood peaks ow and ood volumes increasewith
imperviousness for all ood events; daily peak ows increaseless than
that of ood volume in all ood events due to urbaniza-tion, daily
peak ow discharges and ood volumes of small oodsincreased
proportionally more than those of large oods with thesame
urbanization scenario, implying that small oods and oodvolumes
would be more sensitive to urbanization.
Fourthly, the potential changes in peak discharge and ood
vol-ume with increasing impervious surface showed linear
relation-ships, and the curve slopes of small oods are steeper than
thoseof large oods. The possible reason for this linear
relationship isthat the proportion of urban land use is not high
enough to resultin nonlinear changes in ows.
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-
138 J. Du et al. / Journal of Hydroof urbanization, which must
be considered in watershed manage-ment, water resources planning,
and ood planning for sustainabledevelopment.Dreher, D.W., Price,
H.T., 1997. Reducing the Impacts of Urban Runoff: TheAdvantages of
Alternative Site Design Approaches. Northeastern IllinoisPlanning
Commission, Chicago.
Ferguson, B.K., Suckling, P.W., 1990. Changing rainfallrunoff
relationships in theurbanizing Peachtree Creek watershed, Atlanta,
Georgia. Water Resour. Bull. 26(2), 313322.
Franczyk, J., Chang, H., 2009. The effects of climate change and
urbanization on therunoff of the Rock Creek basin in the Portland
metropolitan area, Oregon, USA.Hydrol. Process. 23, 805815.
Green, G.M., Schweik, C.M., Randolf, J.C., 2005. Retrieving
land-cover changeinformation from Landsat satellite images by
minimizing other sources ofreectance variability. In: Moran, E.F.,
Ostrom, E. (Eds.), Seeing the Forest andthe Trees:
Human-Environment Interactions in Forest Ecosystems. MIT
Press,Cambridge, MA, pp. 131160.
Hejazi, M.I., Markus, M., 2009. Impacts of urbanization and
climate variability onoods in Northeastern Illinois. J. Hydrol.
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431435.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 Universitiesthe
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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J. Du et al. / Journal of Hydrology 464465 (2012) 127139 139
Assessing the effects of urbanization on annual runoff and flood
events using an integrated hydrological modeling system for Qinhuai
River basin, China1 Introduction2 Materials and methods2.1 Study
area and data2.2 Generation of historical land use scenarios2.3
Development of future land use scenarios2.4 Building of
urbanization scenarios2.5 Development of hydrological soil map2.6
Description of HEC-HMS2.7 Construction of HEC-HMS project2.8
Calibration and validation of HEC-HMS
3 Results and discussion3.1 Historical land use change3.2
Projected future land use scenarios3.3 Urbanization scenarios3.4
Calibration and validation of HEC-HMS for long term simulation3.5
Calibration and validation of HEC-HMS for flood events
simulation3.6 Impact of urbanization on mean annual runoff for
198620063.7 Impact of urbanization on annual runoff for typical
hydrological years3.8 The impact of urbanization on flood events3.9
Sensitivity of flood changes to increasing impervious surface
4 Summary and conclusionAcknowledgementReferences
