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Journal of Hydrology 464–465 (2012) 127–139
Contents lists available at SciVerse ScienceDirect
Journal of Hydrology
journal homepage: www.elsevier.com/locate/jhydrol
Assessing the effects of urbanization on annual runoff and flood events using
an 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, China
Earthquake Administration of Guangxi Antonomous Region, Nanning 530022, China
c
Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, NO-0316 Oslo, Norway
b
a r t i c l e
i n f o
Article history:
Received 22 July 2011
Received in revised form 7 June 2012
Accepted 30 June 2012
Available online 20 July 2012
This manuscript was handled by
Konstantine P. Georgakakos, Editor-in-Chief,
with the assistance of Timothy David
Fletcher, Associate Editor
Keywords:
CA-Markov model
HEC-HMS model
Urbanization
Annual runoff
Peak flow
Flood volume
s u m m a r y
This study developed and used an integrated modeling system, coupling a distributed hydrologic and a
dynamic land-use change model, to examine effects of urbanization on annual runoff and flood events
of the Qinhuai River watershed in Jiangsu Province, China. The Hydrologic Engineering Center’s
Hydrologic Modeling System (HEC-HMS) was used to calculate runoff generation and the integrated
Markov Chain and Cellular Automata model (CA-Markov model) was used to develop future land use
maps. The model was calibrated and validated using observed daily streamflow data collected at the
two outlets of watershed. Landsat Thematic Mapper (TM) images from 1988, 1994, 2006, Enhanced Thematic 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 the
watershed experienced conversion of approximately 17% non-urban area to urban area between 1988
and 2009. The urbanization scenarios for various years were developed by overlaying impervious surfaces
of different land use maps to 1988 (as a reference year) map sequentially. The simulation results of HECHMS model for the various urbanization scenarios indicate that annual runoff, daily peak flow, and flood
volume 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 from
3% (1988) to 31% (2018), the mean annual runoff would increase slightly and the annual runoff in the dry
year would increase more than that in the wet year. The daily peak discharge of eight selected floods
would increase from 2.3% to 13.9%. The change trend of flood volumes is similar with that of peak discharge, but with larger percentage changes than that of daily peak flows in all scenarios. Sensitivity analysis revealed that the potential changes in peak discharge and flood volume with increasing impervious
surface showed a linear relationship, and the changes of small floods were larger than those of large
floods with the same impervious increase, indicating that the small floods were more sensitive than large
floods to urbanization. These results suggest that integrating distributed land use change model and distributed 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 for
sustainable development.
Ó 2012 Elsevier B.V. All rights reserved.
1. Introduction
The world population has grown very rapidly over the last
150 years and continues to do so, resulting in impacts on hydrologic resources at both a local and global scale. One of the recent
thrusts in hydrologic modeling is the assessment of the effects of
land use and land cover changes on water resources and floods
(Yang et al., 2012), which are essential for planning and operation
of civil water resource projects, and for early flood warning. The
influence of urbanization as one of the important land use and land
⇑ Corresponding author. Tel.: +47 22 855825; fax: +47 22 854215.
E-mail address: chongyu.xu@geo.uio.no (C.-Y. Xu).
0022-1694/$ - see front matter Ó 2012 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.jhydrol.2012.06.057
cover changes on runoff and floods within watersheds is one of the
main research topics in the past decades.
It is widely recognized that urbanization changes hydrological
processes within watersheds by altering surface infiltration characteristics. The expected results of urbanization include reducing
infiltration, 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; Dougherty
et al., 2006; Ogden et al., 2011). Numerous researchers have used
many methods to simulate, assess, and predict the effects of urbanization on hydrological response of the watersheds. For example,
Tung and Mays (1981) developed a non-linear hydrological system-state variable model to simulate urban rainfall–runoff, and
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examined the variation of each parameter for different levels of
urbanization. Bhaskar (1988) adopted Clark’s instantaneous unit
hydrograph concept to determine the parameters that influence
the effect of urbanization on the watershed. Ferguson and Suckling
(1990) applied polynomial regressive equations of impervious
surfaces to analyze the relationship of runoff to rainfall for total annual flows, low flows and peak flows. Kang et al. (1998) illustrated
the runoff characteristics of urbanization by utilizing the concept
of linear cascading reservoirs. Valeo and Moin (2000) used a model
called TOPURBAN, a revision of TOPMODEL, to observe the interaction between parameters on urbanized watersheds. Cheng and
Wang (2002) developed a method to define the degree of change
in runoff hydrographs for the urbanizing Wu-Tu watershed in
Taiwan. Choi et al. (2003) applied the Cell Based Long Term Hydrological Model (CELTHYM) to evaluate long term hydrologic impacts
caused by land use changes associated with urbanization for a watershed in central Indiana. Huang et al. (2008) used regression
analysis to establish the relationship between hydrograph parameters and peak discharge and their corresponding imperviousness
for the urbanizing Wu-Tu watershed in Taiwan. Franczyk and
Chang (2009) used an ArcView Soil and Water Assessment Tool
(AVSWAT) hydrological model to assess the effects of climate
change and urbanization on the runoff of the Rock Creek basin in
the Portland metropolitan area, Oregon, USA. Lin et al. (2009) assessed the impact of land-use patterns on runoff in watershed
and sub-watershed scales for an urbanized watershed in Taiwan
by combined use of a spatial pattern optimization model (OLPSIM),
the Conversion of Land-Use and its Effects model (CLUE-s) and the
Hydrologic Engineering Center’s Hydrologic Modeling System
(HEC-HMS). Im et al. (2009) applied the MIKE SHE model to quantitatively assess the impact of land use changes (predominantly
urbanization) on hydrology of the Gyeongancheon watershed in
Korea. Li and Wang (2009) used a Long-Term Hydrologic Impact
Assessment (L-THIA) model to evaluate the effect of land use and
land cover change on surface runoff in the Dardenne Creek watershed of St. Louis, Missouri. Chu et al. (2010) used the Conversion
of Land-use and its Effects (CLUE-s) model and Distributed Hydrology-Soil Vegetation Model (DHSVM) to examine hydrologic effects
of various land-use change scenarios in the Wu-Tu watershed in
northern Taiwan.
Distributed models rely on a physically based description of the
runoff generation and the effects of different land covers play an
important role in exploring hydrologic effects of land-use changes
in the catchment. The above-mentioned Mike SHE, SWAT,
HEC-HMS, DHSVM, L-THIA and CELTHYM, for example, have been
extensively used to assess the effects of land use changes (predominantly urbanization) on hydrologic processes. However, most distributed models are commonly used in small watersheds with a
single-outlet, and in our study area, the Qinhuai River basin has
two outlets (bifurcation—a split in the flow in a channel), a suitable
distributed model that can deal with such basins needs to be selected and evaluated. The HEC-HMS is one such model and therefore was selected together with a land-use change model to
explore the hydrological effect of urbanization in the Qinhuai River
basin.
Many methods have been developed to simulate land use
change, such as empirical–statistical models, stochastic models,
conceptual models, and dynamic (process-based) models (Lambin
et al., 2000). Among those, Markov Chain and Cellular Automata
models are most often used. Markov chain models are commonly
used to quantify transition probabilities of multiple land cover categories from discrete time steps; however, there is no spatial component in the modeling outcome. Cellular Automata (CA), on the
other hand, can effectively model proximity to predict spatially explicit 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 character
of Markov chain models and the spatial character of CA models.
The foundation of a CA-Markov model is an initial distribution
and a transition matrix, which assumes that the drivers that produce the detectable patterns of land cover categories will continue
to act in the future as they had been in the past (Briassoulis, 2000).
In this study, the CA-Markov model was used to develop future
land use change scenarios, and based on which the future urbanization scenarios can be constructed.
In this paper, the CA-Markov model and HEC-HMS model system
were used as an integrated system to quantify the annual runoff
and flood response to urbanization. The main objective of this study
was to develop and test the integrated modeling system for analyzing the effects of sub-urban development on runoff and flood events
under urbanization scenarios taken from multi-temporal satellite
imageries for the Qinhuai River basin in China, which is essential
for maintaining an adequate water supply, protecting water quality
and management of flood disasters. The study provides a useful
framework for similar studies in other regions of the world. The primary goal was achieved through the following steps: (1) to develop
an integrated modeling system that couples a distributed hydrologic model and a dynamic land use change model for examining
the effects of urbanization on annual runoff and flood events; (2)
to propose a method which can be used to develop urbanization
scenarios for determining hydrologic response of watersheds to
urbanization; (3) to test the capabilities of HEC-HMS modeling system for simulating daily stream flow in a large basin (in this case, an
area of about 2600 km2); and (4) to explore whether the effects of
suburban development on runoff characteristics of the study area
are 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°190 E
longitude and 31°340 to 32°100 N latitude. It has an area of 2631
square 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 past
decades, resulting in extensive land use changes. Therefore, it is
essential and valuable to assess the hydrologic impacts of land
use changes in the region for the current situation and future
scenarios.
The studied basin lies in the humid climatic region. The mean
annual precipitation is approximately 1047 mm, and the rainy season extends from April to September, with intense precipitation in
summer (June to August). The mean annual temperature is about
15.4 °C.
The land use types are paddy field, woodland, impervious surface, water, and dry land. Among those, paddy field and dry land
are the main land use types (for details see Section 3.1). The main
soil types are yellow–brown soil, purple soil, limestone soil, paddy
soil, and gray fluvo-aquic soil.
Seven raingage stations and two stream flow gauging stations at
the outlets of the basin were used for the study. The watershed
location, elevation, distribution of rainfall and flow gauging stations, and streams are seen in Fig. 1.
The data used in this study were: (a) multi-temporal and multispectral satellite images, representing land use changes in the basin over time; (b) daily rainfall data of the seven raingage stations
for the 21-year period (1986–2006) from the China Meteorological
Data Sharing Service System; (c) daily discharge data of Inner Qinhuai station and Wudingmen station covering the period from January 1986 to December 2006; (d) soil map of the study area on
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Fig. 1. Map of Qinhuai River basin used in this study.
1:75,000 scale; and (e) Digital Elevation Model (DEM) of the
Qinhuai River basin.
2.2. Generation of historical land use scenarios
As the basis for hydrologic impact evaluation of the land use
changes, digital land use maps were generated from a multi-temporal 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), and
20 m resolution China–Brazil Earth Resources Satellite (CBERS) image from 2009 were used in this study. While the sensors offer different spatial and spectral resolutions, such multispectral datasets
are often unavoidable in studies spanning over several decades and
have been successfully applied in other regions (Zoran and Anderson, 2006).
Image pre-processing was carried out in ERDAS Imagine 9.3.
The satellite images were generated by applying coefficients for
radiometric calibration, geometric rectification and projected to
the Universal Transverse Mercator (UTM) ground coordinates with
a spatial resampling of 30 m. Geometric rectification was carried
out on Landsat images from 1988, 1994, 2003, 2006 and CBERS image from 2009 using the ETM+ from 2001 as a base-map, and nearest neighbor resampling algorithm, with root mean square (RMS)
error of less than 0.5 pixels via image-to-image registration. Radiometric calibration and atmospheric correction were carried out to
correct for sensor drift, differences due to variation in the solar angle, and atmospheric effects (Green et al., 2005).
The supervised classification method with maximum likelihood
clustering and DEM data were employed for image classification as
a hybrid method to generate land use maps and post-classification
analysis was applied to create the trend map of land use changes.
Land use categories were paddy field, dry land, woodland, impervious surface and water. Pure pixels, rather than mixed pixels, were
selected as training samples. Mixed classes such as paddy field and
woodland were separated with the aid of DEM data. Ground truthing was performed to assist in the imagery classification and to
validate the final results. Each image was classified following the
same method.
Overall accuracy and Kappa value were selected as evaluation
criteria for the classification. An error matrix was generated based
on test samples for each land use map. The columns of error matrix represent the reference data by ground truthing, while the
rows indicate the classified land use category. The overall accuracy is computed by dividing the total correct pixels (i.e., the
sum of the major diagonal) by the total number of pixels in the
error matrix (Russell, 1991). Kappa analysis is a discrete multivariate technique used in accuracy assessment, Kappa value (Kap) is
computed as
K ap ¼
N
Pr
Pr
xi
i¼1 xii Pr i¼1
2
N i¼1 xiþ xþi
xþi
ð1Þ
where r is the number of rows in the matrix, xii is the observation in
row i and column i, xi+ and x+i are the marginal totals of row i and
column 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 between 1 and 1. If the test samples are in perfect agreement (all
the same between classification results and predicted results), values for the overall accuracy and Kap equal to 1.
In this study, the overall classification accuracy of each image
was over 89% with kappa values over 0.79, meeting the accuracy
requirements. 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 use
change scenarios. A Markov chain is a stochastic process that consists of a finite number of states of a system in discrete time steps
and some known transition probabilities Pij (the probability of
that particular system moving from time step i to time step j).
The value of the stochastic process at time t, St, depends only
on its value at time t 1, St1, and not on the sequence of values
St2, St3, . . ., S0. Land use change can be regarded as a stochastic
process and different categories are the states of a chain. The
Markov chain equation was constructed using the land use distributions at the time step i (Si), and at the time step j (Sj) of a discrete time period as well as transition probabilities Pij
representing the probabilities of each land use category changing
to every other category (or remaining the same) during that period. Pij equation is as follows:
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Fig. 2. The land use maps of the basin.
2
P11
6P
6 21
Pij ¼ 6
6 ..
4.
Pn1
P12
P22
..
.
Pn2
..
.
3
P1n
P2n 7
7
70 6 Pij < 1 and
... 7
5
N
X
Pij ¼ 1 ði; j ¼ 1; 2 nÞ
i¼1;j¼1
Pnn
ð2Þ
Future land use can be modeled on the basis of the preceding
state and a matrix of actual transition probabilities between the
states. However, there is no spatial component in the modeling
outcome. Cellular automata (CA), on the other hand, can effectively
model proximity, i.e., areas will have a higher tendency to change
to the land use category of the neighboring cells (Balzter et al.,
1998). CA works as a dynamic and spatially explicit modeling approach, in which the state of each cell at time t + 1 is determined
by the state of its neighboring cells at time t according to the
pre-defined transition rules. Five components were included: (a)
a space composed of discrete cells, (b) a finite set of possible states
associated to every cell, (c) a neighborhood of adjacent cells whose
state influences the central cell, (d) uniform transition rules
through time and space, and (e) a discrete time step to which the
system is updated (Wolfram, 1984). The hybrid CA-Markov model
(Cellular Automata-Markov), integrating the merits of the Markov
chain and CA models, can reconstruct the spatial patterns of future
land 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 software
IDRISI (Clark-Labs, 2003). Land use of 2009 has been built with the
trend of land use change during 2003–2006. The detailed procedure 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 and
2006 based on Markov module of the software. The transition
probability matrix is a text file that records the probability of each
land use category changing to every other category. The transition
areas matrix is a text file that records the number of pixels that are
expected to change from each land use type to other land use type
over the specified number of time units. The conditional probability images report the probability of each land cover type to be
found 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 land
use category with values are stretched to a range of 0–255. The
probability maps created by the Markov module were used as
the suitability map.
Third, a 5 5 contiguity filter was used to generate a spatial explicit contiguity-weighting factor to change the state of cells based
on its neighbors. The filter emphasized that the spatial scale of
150 m 150 m around a cell would have more significant impacts
on land use change of the cell.
Fourth, 3-year loops times were used for the CA model to predict land use. Then the land use map of 2009 was developed using
the land use map of 2006 as the baseline.
The predicted land use map of 2009 (Fig. 2e) was compared
with the classification of CBERS image from 2009 (Fig. 2d) to test
the model accuracy according to the area of each land use category.
The classification of the CBERS image was considered as the actual
land use distribution; an error matrix was generated based on 400
test samples.
In the same way, with the transition matrix generated between
2003 and 2006, a 6-year loop time and a 12-year loop time were
used to predict the land use map of 2012 and 2018 using the land
use map of 2006 as the baseline, respectively.
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2.4. Building of urbanization scenarios
In order to analyze hydrological effects of urbanization and exclude complicated effects caused by all other land use changes, the
urbanization scenarios are built following three steps: first, the
land use map of 1988 was chosen as a reference; second, impervious surfaces (urban areas) were extracted from land use maps of
1994, 2001, 2003, 2006, 2009, 2012, and 2018; and third, impervious surfaces (urban areas) extracted in step two were overlaid to
the land use map of 1988 to produce urbanization scenarios for
1994, 2001, 2003, 2006, 2009, 2012, and 2018 respectively. In such
a way, the urbanization scenarios only differ in the size of urban
areas while the rest of the catchment remain the same land use
type as in 1988. That is to say, there could only be transitions of
other land use types to impervious surfaces, and no inter exchanges among other land use types within the urbanization scenario series, therefore the hydrologic effect of urbanization could
then be assessed avoiding other effects caused by all land use
changes.
2.5. Development of hydrological soil map
Soil data of the study area were generated from existing Soil
Survey maps at a scale of 1:75,000. Soil maps were rectified and
mosaicked, so that the study area was extracted by sub-setting it
from the full map. Boundaries of different soil textures were digitized and various polygons were assigned to represent different
soil categories such as yellow–brown soil, purple soil, limestone
soil, paddy soil, and gray fluvo-aquic soil. According to the rules
of hydrologic soil group classifications developed by the US Natural
Resource Conservation Service (NRCS), only hydrologic soil groups
B (paddy soil, purple soil) and C (yellow–brown soil, limestone soil
and gray fluvo-aquic soil) are presented in the basin (Fig. 3), indicating a moderate infiltration rate and a slow infiltration rate
respectively when thoroughly wetted.
Engineers Hydrologic Engineering Centre (HEC). HEC-HMS uses
separate sub-models to represent each component of the runoff
process, including models that compute rainfall losses, runoff generation, base flow, and channel routing. Each model run combines
the Basin Model, the Precipitation Model, and the Control Model.
The Basin Model contains the basin and routing parameters of
the model, as well as connectivity data for the basin. The Precipitation Model contains the rainfall data for the model. The Control
Model contains all the timing information for the model. The user
may specify different data sets for each model and then the hydrologic simulation is completed by using of data set for the Basin
Model, the Precipitation Model, and the Control Model. The details
of model structures and various processes involved are given in the
Technical Reference Manual (USACE-HEC, 2000) and the User’s
Manual (USACE-HEC, 2008) of HEC-HMS. A brief description of
models used in this study is provided here for completeness only.
HEC-HMS categorizes all land types and water in a watershed as
either directly connected impervious surface or pervious surface.
Precipitation on directly connected impervious surface runs off
with no volume losses. Precipitation on the pervious surfaces is
subject to losses (Jha and Mahana, 2010). The SCS-CN loss model
was used in the present study, which estimates precipitation excess as a function of cumulative precipitation, soil cover, land
use, and antecedent moisture using the following equation (Singh,
1994):
Pe ¼
In this study, we used the hydrological model, HEC-HMS, to calculate the runoff from the resulting landscapes. HEC-HMS is hydrologic modeling software developed by the US Army Corps of
ð3Þ
where Pe is accumulated precipitation excess at time t, P is accumulated rainfall depth at time t, Ia is the initial abstraction (initial loss),
and S is potential maximum retention, a measure of the ability of a
watershed to abstract and retain storm precipitation.
The SCS developed an empirical relationship between Ia and S as
Ia = 0.2S. Therefore, the cumulative excess at time t is given as:
Pe ¼
2.6. Description of HEC-HMS
ðP Ia Þ2
P Ia þ S
ðP 0:2SÞ2
P þ 0:8S
ð4Þ
The maximum retention (S) is determined using the following equation (SI system):
S¼
25; 400 254CN
CN
ð5Þ
where CN is the SCS curve number. It is an index that represents the
combination of hydrologic soil group, land use classes, and antecedent moisture conditions.
The Clark unit hydrograph (Clark UH) model has been applied
for estimating direct runoff. Clark’s model derives a watershed
UH by explicitly representing two critical processes in the transformation of excess precipitation to runoff: Translation of the excess
from its origin throughout the drainage system to the watershed
outlet and attenuation of the magnitude of the discharge as the excess is stored throughout the watershed. Application of the Clark
model requires properties of the time-area histogram and a storage
coefficient. The time-area relationship can be represented by a
smooth function requiring only one parameter, the time of concentration. The storage coefficient is an index of the temporary storage
Table 1
Curve number for hydrologic soil groups B and C.
Fig. 3. Hydrologic soil map of the basin.
Land use
B
C
Paddy field
Woodland
Impervious surface
Water
Dry land
76
64
98
95
76
84
73
98
95
82
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Fig. 4. Sketch map of hydrologic elements in Basin Model.
of precipitation excess in the watershed as it drains to the outlet
point. The two parameters can be estimated via calibration if
gauged precipitation and streamflow data are available or by equations presented in Bedient and Huber (1992).
In HEC-HMS, the baseflow model is applied both at the start of
simulation of a storm event, and later in the event as the delayed
subsurface flow reaches the watershed channels. The recession
model adopted in present study explains the drainage from natural
storage in a watershed. It defines the relationship of the baseflow
Qt at any time t to an initial value Q0 as:
Q t ¼ Q 0Kt
ð6Þ
Q 2 ¼ ðc1 c2 ÞI1 þ ð1 c1 ÞQ 1 þ c2 I2
2 Dt
c1 ¼
2 K ð1 XÞ þ Dt
Dt 2 K X
c2 ¼
2 K ð1 XÞ þ Dt
ð7Þ
where I1, I2 are the inflows to the routing reach at the beginning and
end of computation interval respectively, Q1 and Q2 are the outflows
from the routing reach at the beginning and end of computation
interval respectively, K is the travel time through the reach, X is
the Muskingum weighting factor (0 6 X 6 0.5), and Dt is the length
of computation interval.
2.7. Construction of HEC-HMS project
where K is an exponential decay constant. A threshold flow, after
the peak of the direct runoff, should be specified either as a flow
rate or as a ratio to the computed peak flow when applying recession model (Jha and Mahana, 2010).
The Muskingum method was adopted to compute outflow from
each reach. The method uses the following equation:
The project containing the Basin Model, the Precipitation Model
and the Control Model was created. The Basin Model was built
based on hydrologic elements such as sub-basin, reach, diversion,
junction, reservoir, source and sink, and hydrologic models corresponding to each element. The basin and sub-basin boundaries as
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Table 2
Land use structures from 1988 to 2009(%).
Year
Impervious surface
Paddy field
Water
Woodland
Dry land
1988
1994
2001
2003
2006
2009
3
5
7
8
12
20
48
47
45
44
42
40
4
4
4
4
4
3
19
17
18
18
17
15
26
27
26
26
25
22
Table 3
Future land use scenarios predicted by the CA-Markov model (%).
Year
Impervious surface
Paddy field
Water
Woodland
Dry land
2012
2018
23
31
39
34
3
3
14
13
21
19
error as the objective function. Validation was then performed;
parameters used during calibration were not changed during model validation. HEC-HMS was validated for the 1999–2003 simulation using land use data of 2001 and rainfall data of 1999–2003,
and for 2004–2006 simulation using land use data of 2006 and
rainfall data of 2004–2006.
In order to assess the urbanization effects on flood flow, fourteen flood events with daily peak discharge greater than 500 m3/
s and two other smaller flood events during 1986–2006 were selected for calibration and validation. Four flood events with different peak discharges were selected for model calibration. The
calibration parameters for flood events simulation were same as
those for long-term simulation. The optimized parameter sets for
each calibrated flood events were obtained by selecting peakweighted root mean square error as the objective function and
using the Nelder and Mead simplex search algorithm provided by
HEC-HMS.
well as stream networks needed by the Basin Model were delineated using terrain processing module of ArcHydro Tools software
based on DEM data obtained from existing 1:50,000 scale contour
map. The initial values of the model parameters were determined
by using the default values given by HEC-HMS. The land use and
soil maps of the basin were used to assign CN (Curve Number) values to each grid (30 m 30 m resolution) with the help of HECGeoHMS Project View, referring to the standard table provided
by SCS-USA (McCuen, 1998). Weighted CN values were calculated
for each sub-basin with averaging method in the spatial analyst
module of ArcGIS. Curve Numbers ranged from approximately
64–98 for all sub-basins in this study area (Table 1). Fig. 4 shows
the hydrologic elements in the Basin Model.
The Precipitation Model was set up by putting in daily rainfall
data for each sub-basin, which were calculated by using nearest
neighbor method based on the point rainfall values observed at
the seven raingage stations. The Control Model containing all the
timing information for the model was built by determining time
steps, start and stop date, and times of the simulation.
3. Results and discussion
2.8. Calibration and validation of HEC-HMS
3.2. Projected future land use scenarios
In this study, the HEC-HMS model was calibrated and evaluated
using a split sample procedure against streamflow data collected at
the outlets of the watershed. The objective of the model calibration
was to match simulated daily runoff with the observed data with
different 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 assess model performance. To calibrate and verify the HEC-HMS
model, 21-year (1986–2006) streamflow and precipitation data
were used for the study watershed. The observed runoff dataset
was divided into a calibration period (1986–1998) and a verification period (1999–2006) based on the land use data years 1988,
1994, 2001, and 2006. For model calibration, land use data for
1988 and rainfall data for 1986–1992 were used for 1986–1992
simulation, and land use data for 1994 and rainfall data for
1993–1998 were used for 1993–1998 simulation.
Initial abstraction, time of concentration, storage coefficient,
recession constant, baseflow threshold ratio to peak, Muskingum
weighting factor and travel time were considered as HEC-HMS calibration parameters. A series of model parameters sets was estimated using automated optimization tool provided by HEC-HMS
by selecting several objective functions, and model efficiency (E)
for whole calibration period was computed for each set of parameters to examine the calibration results. The calibrated model
parameters were obtained using peak-weighted root mean square
Land use scenarios of 2012 and 2018 were predicted with the
assumption that the drivers of pre-2006 are still acting on the land
use, and no other policy arrests this trend. It must be emphasized
that the Markov values do not represent realistic future states for
the basin. Rather, they are direct equivalents of land use changes
that occurred in a given time (Michael and John, 1994). The predicted land use maps suggest continuing rapid increases of impervious surface from 23% to 31% with very high losses of paddy field
during 2012–2018 (Table 3). Impervious surface area will become
the second main land use category and other categories represent
trends of decline, confirming that urbanization is one of the most
important driving forces resulting in the general trends in land
use change in future.
3.1. Historical land use change
The land use changes from 1988 to 2009 are presented in Table
2. During 1988–2009, paddy field is the main land use type covering over 40% of the total areas, and the second main land use category is dry land, which occupied over 22%. Subsequently, the
woodland occupied over 15%, with water occupying the remainder.
The urban area development has been recognized for over
21 years, and a high rate of urban expansion emerged after 2003
at the expense of the amount of other land use categories, especially the paddy field. From the year 1988 to 2003, the impervious
surface area increased from 3% to 8%; however, it increased to 20%
in 2009. On the other hand, the paddy field decreased substantially
from 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, woodland
represented an increasing trend during 1994–2003.
Table 4
The land use structures of each urbanization scenarios (%).
Year
Impervious ratio
Paddy field
Water
Woodland
Dry land
1988
1994
2001
2003
2006
2009
2012
2018
3
6
8
9
14
20
24
31
48
46
45
45
42
39
38
33
4
4
4
4
4
4
4
3
19
19
18
18
16
15
14
13
26
25
25
25
24
22
21
19
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Table 5
Calibrated 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
Sub2
Sub3
Sub4
Sub5
Sub6
Sub7
Sub8
Sub9
Sub10
Sub11
Sub12
Sub13
Sub14
Sub15
Sub16
Sub17
Sub18
1.03
1.03
1.00
1.03
0.10
1.00
1.03
1.00
1.00
1.03
1.03
1.03
0.50
0.50
0.50
0.10
1.03
1.03
1.03
1.03
1.00
1.03
0.10
1.00
1.03
1.00
1.00
1.03
1.03
1.03
0.10
0.10
0.10
1.03
1.03
1.03
0.90
0.95
0.10
0.90
0.10
0.10
0.90
0.10
0.10
0.90
0.90
0.10
0.95
0.10
0.10
0.10
0.95
0.10
1.00
0.88
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.60
0.88
0.01
0.01
0.01
0.01
0.01
0.99
0.01
3.3. Urbanization scenarios
The results of the urbanization scenarios are listed in Table 4. It
can be seen that there are slight increases in impervious ratio for
each urbanization scenario compared to the corresponding land
use scenario and that the other land use categories correspondingly 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 0.79
and 0.78, respectively; the simulated mean annual runoff is
389 mm with a relative error of 13.3%. The R and E of the validation period (1998–2006) for daily runoff were 0.79 and 0.77,
respectively; the simulated mean annual runoff is 460 mm with
a relative error of 10.4%. The calibrated initial abstraction of all
sub-basins is 15 mm, and the other calibrated parameters of subbasins and sub-reaches are shown in Tables 5 and 6. It can be seen
from these tables that the values of the same parameter for sub-basins and reaches change considerably, which is the result of automatic optimization. Comparison of observed and simulated
discharges of calibration and validation periods is shown in Figs.
5 and 6.
These results show that the model performance was satisfactory during both calibration and validation periods, implying that
the selected models from HEC-HMS were applicable to the Qinhuai
River 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 flood
event simulation were the same as for long term simulation.
Table 6
Calibrated subreach parameters.
Reach
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
R15
R16
R17
R18
R19
R20
R21
R22
R23
Average
Long term simulation
Medium flood simulation
Large flood simulation
Muskingum travel
time (h)
Muskingum weighting
factor
Muskingum travel
time (h)
Muskingum weighting
factor
Muskingum travel
time (h)
Muskingum weighting
factor
100.0
3.0
150.0
150.0
5.0
50.0
0.1
1.0
5.0
6.5
20.0
1.0
10.0
25.0
10.0
90.0
1.0
150.0
1.0
30.0
0.1
5.0
40.0
37.2
0.30
0.30
0.30
0.30
0.10
0.10
0.40
0.10
0.10
0.01
0.20
0.01
0.01
0.10
0.30
0.15
0.10
0.20
0.30
0.01
0.30
0.30
0.30
0.19
100.5
2.9
100.0
100.0
4.9
33.3
0.1
1.0
4.9
6.4
13.3
1.0
9.8
16.7
9.8
60.0
1.0
150.0
1.0
20.0
0.1
4.9
39.2
29.6
0.29
0.45
0.20
0.29
0.07
0.07
0.50
0.15
0.15
0.02
0.13
0.02
0.02
0.15
0.45
0.10
0.15
0.20
0.45
0.01
0.29
0.20
0.45
0.21
102.0
4.4
101.5
101.3
4.9
33.8
0.1
1.0
4.9
11.0
30.1
1.4
22.2
10.9
9.8
60.8
1.0
44.5
1.3
20.1
0.1
4.7
39.5
26.6
0.50
0.29
0.20
0.29
0.03
0.06
0.50
0.05
0.05
0.01
0.13
0.02
0.01
0.07
0.29
0.10
0.07
0.13
0.29
0.01
0.28
0.06
0.19
0.16
Author's personal copy
135
below 20% for most events. The mean efficiency was 0.81, and in
10 of the 16 flood hydrographs the efficiency was higher than
0.8; the mean correlation coefficient was 0.89, and was greater
than 0.8 in 15 of the 16 flood hydrographs. These results indicate
that the selected models from HEC-HMS were suitable for flood
event simulation in the catchment.
0
2200
2000
Simulated
Observed
Rainfall
1500
Rainfall (mm/day)
3
Stream flow (m /sec)
J. Du et al. / Journal of Hydrology 464–465 (2012) 127–139
200
400
1000
600
500
0
1-Jan-1990
1-Jul-1990
1-Jan-1991
1-Jul-1991
3.6. Impact of urbanization on mean annual runoff for 1986–2006
800
31-Dec-1991
Date
Fig. 5. Comparison of daily observed and simulated stream flow selected from
calibration period.
3
Stream flow (m /sec)
Simulated
Observed
Rainfall
1200
200
800
400
400
0
1-Jan-2003
1-Jul-2003
1-Jan-2004
1-Jul-2004
Rainfall (mm/day)
0
1600
600
31-Dec-2004
Date
Fig. 6. Comparison of daily observed and simulated stream flow selected from
validation period.
However, the calibrated values of sub-reach parameters for flood
event were different to those of the long-term simulation; and
parameter values for medium flood events were also different to
those for large flood events (Table 6). It is seen that the average values of Muskingum travel time and weighting factor of each subreach for medium flood events are greater than those for large
flood events, which is reasonable because the travel time of large
flood events will be shorter and the weighting factor smaller. The
calibration and validation results for flood events are listed in Table
7. The comparison of observed and simulated discharges of each
flood event is shown in Fig. 7. It is seen that the simulated flood
hydrographs demonstrate a good agreement with the observed
hydrographs for most flood events, except flood number 199806.
The relative error of simulated peak flow and flood volume was
Long-term simulation was conducted to estimate the impact of
urbanization on runoff under the same meteorological conditions
as 1986–2006. HEC-HMS was run for 21 years without changing
the calibration parameters, for urbanization scenarios based on
land use data of 1988, 1994, 2001, 2006, 2009, 2012, and 2018.
Table 8 summarizes the changes in mean annual runoff depth
under different urbanization scenarios. Mean annual runoff is predicted to hardly change, with an increase of only 0.2% when the
impervious ratio increased from 3% to 31%, which was consistent
with the results of several studies in other regions (Choi and Deal,
2008; Franczyk and Chang, 2009). Choi and Deal (2008) studied
land use change impact on the hydrology of the Kishwaukee River
basin (KRB) in the Midwestern USA and found that the land use
scenarios result in small change in total runoff. Even under the
Uber scenario which is associated with very high population
growth, mean annual runoff has been predicted to increase by only
1.7% 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 runoff
depths, respectively. A possible explanation for such phenomena is
that when impervious area increases, the direct runoff increases
while the baseflow decreases, so that the total runoff would not increase considerably. Another reason might arise from using SCS-CN
method for loss calculation; the original SCS-CN method is an infiltration loss model for single storm that does not account for evaporation and evapotranspiration, which might cause some errors in
long term simulation. The error caused by ignoring evaporation is
expected to increase as the impervious surface decreases.
3.7. Impact of urbanization on annual runoff for typical hydrological
years
An analysis was conducted between urbanization scenarios and
annual rainfall amounts to determine how annual rainfall amount
interacts with urbanization effects on runoff. Three typical hydro-
Table 7
Summary of calibration and validation results for flood simulation at daily step.
*
Flood
No.
Observed peak flow
(m3/s)
Simulated peak
flow (m3/s)
Relative peak flow
error (%)
Observed flood
volume (mm)
Simulated flood
volume (mm)
Relative flood volume
error (%)
R
E
198706*
198708
198806*
198906*
198908
199106*
199107
199603
199606
199806
199906
199907
200206
200306
200406
200607
Average
838
704
376
560
764
1280
1262
246
884
583
630.3
878
806
1106
798
595
685
732
370
647
808
1541
1362
204
735
532
460
754
979
1115
871
556
18
4
2
4
6
20
8
17
17
9
27
14
21
2
9
7
228
131
43
106
110
322
521
24
173
128
65
132
165
352
120
112
221
187
40
99
126
348
653
21
210
126
51
142
229
483
106
101
3
43
7
7
15
8
25
15
21
2
22
8
39
38
11
10
0.94
0.85
0.82
0.95
0.92
0.85
0.93
0.99
0.93
0.63
0.97
0.83
0.97
0.85
0.89
0.90
0.89
0.92
0.72
0.79
0.95
0.90
0.83
0.83
0.90
0.72
0.46
0.79
0.73
0.87
0.75
0.89
0.81
0.81
calibrated floods.
Author's personal copy
8
600
400
200
0
12
800
400
0
16
0
4
8
500
250
0
8
12
16
20
150
0
4
400
200
0
12
16
12
20
24
900
600
300
0
300
0
0
4
3
0
4
8
Storm 199906
420
280
140
0
4
8
100
50
0
0
4
8
600
400
200
0
Time (day)
Simulated
8
12
12
1000
Storm 199907
800
600
400
200
0
0
4
8
12
16
Time (day)
Storm 200406
4
24
150
12
800
0
20
Storm 199603
200
Time (day)
1000
16
Time (day)
700
560
12
250
8 12 16 20 24 28 32
0
0 4 8 12 16 20 24 28 32 36
Time (day)
0
Time (day)
600
16
Storm 200306
3
Stream flow (m /s)
3
Stream flow (m /s)
600
8
1200
20
900
Time (day)
Storm 200206
4
8
16
Time (day)
300
0
12
Storm 199107
1200
24
Storm 199806
24
1000
0
20
450
Time (day)
800
16
600
3
Stream flow (m /s)
3
Stream flow (m /s)
Storm 199606
750
8
1500
Time (day)
1000
4
12
3
Storm 199106
1200
Time (day)
0
4
200
600
Storm 200607
3
8
0
400
Time (day)
3
4
24
1600
3
Storm 198908
0
20
Stream flow (m /s)
1000
800
16
Time (day)
Stream flow (m /s)
3
Stream flow (m /s)
Time (day)
12
0
Storm 198906
600
3
4
100
Stream flow (m /s)
3
0
200
800
Stream flow (m /s)
8 12 16 20 24 28 32
Stream flow (m /s)
4
0
3
0
200
Storm 198806
300
3
0
400
400
Stream flow (m /s)
200
600
Stream flow (m /s)
400
Storm 198708
Stream flow (m /s)
600
800
3
Storm 198706
800
Stream flow (m /s)
3
1000
Stream flow (m /s)
J. Du et al. / Journal of Hydrology 464–465 (2012) 127–139
Stream flow (m /s)
136
16
20
450
300
150
0
0
Time (day)
Observed
4
8
12
16
Time (day)
Fig. 7. Comparison of observed and simulated stream flow of 16 flood events.
Table 8
Simulated annual runoff under different urbanization scenarios.
Urbanization
scenarios
Impervious
ratio (%)
Long term
Simulated
annual runoff
(mm)
Increased
from1988
(%)
Simulated
annual runoff
(mm)
Wet year
Increased
from1988
(%)
Simulated
annual runoff
(mm)
Increased
from1988
(%)
Simulated
annual runoff
(mm)
Increased
from1988
(%)
1988
1994
2001
2003
2006
2009
2012
2018
3
6
8
9
14
20
24
31
431
431
431
431
432
432
432
432
0.0
0.0
0.0
0.2
0.2
0.2
0.2
1384
1384
1386
1387
1389
1392
1394
1397
0.0
0.1
0.2
0.4
0.6
0.7
0.9
261
261
262
263
264
265
266
268
0.2
0.5
0.6
1.2
1.7
2.0
2.6
90
91
91
92
93
94
94
95
1.1
1.1
2.2
3.3
4.4
4.4
5.6
logical years (dry year with annual precipitation exceedence probability of 90%, normal year with annual precipitation exceedence
probability of 50%, and wet year with annual runoff exceedence
probability of 10%) are selected, which are 1994, 2000 and 1991
with annual precipitations of 695, 1055 and 1913 mm respectively.
Annual runoff depth increases very slightly with increasing
impervious surface area for all three typical hydrological years (Table 8). The runoff increase percentages for the dry year are a little
bit bigger than that for the wet year under the same urbanization
scenarios; even when impervious ratio reaches 31%, the annual
runoff increased 5.6% in the dry year. Considering the model uncertainty and that the largest increase in annual runoff was 13 mm
comparing with annual runoff 1384 mm at the baseline year,
urbanization has little effect on annual runoff, as explained at the
end of Section 3.6.
Normal year
Dry year
3.8. The impact of urbanization on flood events
The calibrated HEC-HMS model was applied to each of the
urbanization scenarios to assess the effects of urbanization on
flood events in the watershed. Eight flood events with different
magnitude peak discharges were selected to assess the potential
change in response to urbanization. The simulation results are presented in Tables 9 and 10, where it can be seen that (1) urban
developments affect peak flows and runoff volumes more than
long-term runoff, and (2) the flood volumes increased slightly
more than that of flood peaks for the same increase of impervious
surface ratio. These results agreed with those from Dreher and
Price (1997), Im et al. (2003) and Hejazi and Markus (2009). The
larger percentage increase in flood volume than that in flood peak
would increase the duration of flood inundation.
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137
J. Du et al. / Journal of Hydrology 464–465 (2012) 127–139
Table 9
Peak flow response to urbanization.
Scenarios
Impervious ratio
1988
1994
2001
2003
2006
2009
2012
2018
3
6
8
9
14
20
24
31
198706
Qp
685
687
691
693
700
709
716
726
200406
4
Qp
0.3
0.9
1.2
2.2
3.5
4.5
6.0
867
869
872
874
879
886
891
898
19910607
200306
4
Qp
4
Qp
0.2
0.6
0.8
1.4
2.2
2.8
3.6
1541
1543
1546
1547
1555
1562
1567
1576
0.1
0.3
0.4
0.9
1.4
1.7
2.3
1111
1113
1117
1119
1124
1133
1139
1147
198906
4
Qp
0.2
0.5
0.7
1.2
2.0
2.5
3.2
647
649
653
656
663
672
679
688
200607
4
Qp
0.3
0.9
1.4
2.5
3.9
4.9
6.3
551
551
554
554
557
561
563
567
199603
4
Qp
0.0
0.5
0.5
1.1
1.8
2.2
2.9
202
203
206
207
212
218
223
230
198806
Average (%)
4
Qp
4
0.5
2.0
2.5
5.0
7.9
10.4
13.9
370
372
376
377
383
390
396
405
0.5
1.6
1.9
3.5
5.4
7.0
9.5
0.3
0.9
1.2
2.4
3.5
4.5
6.0
Qp = Simulated peak flow (m3/s); 4 = increased from1988 (%).
Table 10
Flood volume response to urbanization.
Scenarios
1988
1994
2001
2003
2006
2009
2012
2018
Impervious ratio
3
6
8
9
14
20
24
31
19910607
200306
199603
198806
Vp
198706
4
Vp
200406
4
Vp
4
Vp
4
Vp
198906
4
Vp
200607
4
Vp
4
Vp
4
221
222
224
224
226
229
231
234
0.5
1.4
1.4
2.3
4.1
4.5
5.9
105
106
107
107
109
111
112
114
1.0
1.9
1.9
3.8
5.7
6.7
8.6
348
349
351
351
353
357
359
363
0.3
0.9
0.9
1.4
2.6
3.2
4.3
481
482
485
485
488
492
496
499
0.2
0.8
0.8
1.5
2.3
3.1
3.7
99
100
100
101
102
103
105
106
1.0
1.0
2.0
3.0
4.0
6.1
7.1
100
100
101
101
101
102
103
103
0.0
1.0
1.0
1.0
2.0
3.0
3.0
20
20
21
21
22
23
24
24
0.0
5.0
5.0
10.0
15.0
20.0
20.0
40
40
41
41
42
43
44
46
0.0
2.5
2.5
5.0
7.5
10.0
15.0
Average(%)
0.4
1.8
1.9
3.5
5.4
7.1
8.5
Vp = Simulated flood volume (mm); 4 = increased from 1988 (%).
3.9. Sensitivity of flood changes to increasing impervious surface
Peak flow increase (%)
15
Flood 199603
Flood 198706
Flood 199106
Linear Fit of flood 199603
Linear Fit of flood 198706
Linear Fit of flood 199106
12
9
6
3
0
0
10
15
20
25
30
35
25
30
35
21
Flood 199603
Flood 198706
Flood 199106
Linear Fit of flood 199603
Linear Fit of flood 198706
Linear Fit of flood 199106
18
15
12
9
6
3
0
0
The sensitivity of peak discharge and flood volume to increasing
urbanization (impervious surface) was also examined. Fig. 8 shows
the simulated daily peak discharge and flood volume with increasing impervious surface for various event magnitudes. All the curves
are close to linear, and the curve slopes of small floods are steeper
than those of large floods, which again means that small floods are
more sensitive to urbanization than are large floods. These results
5
Impervious ratio (%)
Flood volume increase (%)
The results in Tables 9 and 10 also show that daily flood peak
discharges and flood volumes of small flood events increased due
to urbanization by a larger proportion than did those of large flood
events, which means that small floods are more sensitive to urbanization than large floods. This finding agrees well with the literature which reports that flood magnitudes of rare events are less
sensitive to increases in watershed impervious surface cover than
those with shorter recurrence intervals (Hollis, 1975; Booth,
1988; Konrad, 2003). Such phenomena were explained by Beighley
et al. (2003), who noted that for smaller events, near the threshold
of runoff, increased imperviousness resulted in significantly more
runoff. For larger storms, the effect of increased imperviousness
was minimal because a larger fraction of the watershed ‘‘saturates’’
relatively early during the event, essentially diminishing the effects of initial storage capacity provided by non-urban lands. For
a given increase in impervious area, the percent increase in peak
discharge and runoff volume generally decreases with increasing
rainfall magnitude. However, Sheng and Wilson (2009) found that
for 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 urban development, and smaller basins experience relatively greater
impacts than larger ones. It should also be noted that the relative
increase of flood peak and flood volume depends not only on the
relative increase of impervious surface, but also on the degree of
urbanization and geographic region.
5
10
15
20
Impervious ratio (%)
Fig. 8. The potential changes in peak flow and flood volume with increasing
impervious ratio for varied amplitudes of floods.
are in agreement with those from Changnon et al. (1996), Bhaduri
et al. (2001) and Choi and Deal (2008), but not with that from Brun
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J. Du et al. / Journal of Hydrology 464–465 (2012) 127–139
and Band (2000) and Wissmar et al. (2004). In the study of Brun
and Band (2000), a logistic relationship between runoff ratio and
imperviousness, and an exponential relationship between base
flow and imperviousness was found when imperviousness was increased up to 90%. Wissmar et al. (2004) found that the magnitude
of flood flows for urban watersheds in the lower Cedar River drainage in the US tends to increase nonlinearly when impervious ratios
reach 43–74% levels. In the present study, the percentage of urban
land use is not high enough to result in nonlinear changes in flows.
4. Summary and conclusion
This paper has attempted to connect a distributed hydrological
model and a dynamic land use change model as a tool for examining urbanization influences on annual runoff and flood of the
Qinhuai River watershed in Jiangsu Province, China. The hydrological model based on Hydrologic Engineering Center’s Hydrologic
Modeling System (HEC-HMS) was calibrated and validated, and
repeatedly run with various urbanization scenarios. The urbanization scenarios were developed based on historical land use maps
obtained from TM images and CBERS image, and future land use
maps were generated by an integrated Markov Chain and Cellular
Automata model (CA-Markov model). The following conclusions
are drawn from the study.
Firstly, there were slight increases in mean annual runoff of the
whole watershed as a response to urbanization, which implies that
the region is not likely to undergo significant changes in the availability of surface water resource due to future urban growth
pressures.
Secondly, the changes of annual runoff in dry years are proportionally greater than in wet years, which means that availability of
surface water resource in dry years is more sensitive to urbanization.
Thirdly, the daily flood peaks flow and flood volumes increase
with imperviousness for all flood events; daily peak flows increase
less than that of flood volume in all flood events due to urbanization, daily peak flow discharges and flood volumes of small floods
increased proportionally more than those of large floods with the
same urbanization scenario, implying that small floods and flood
volumes would be more sensitive to urbanization.
Fourthly, the potential changes in peak discharge and flood volume with increasing impervious surface showed linear relationships, and the curve slopes of small floods are steeper than those
of large floods. The possible reason for this linear relationship is
that the proportion of urban land use is not high enough to result
in nonlinear changes in flows.
It is worth noting that the CA-Markov model was used under
the assumption that the land management policy will remain the
same and that the hydrologic response of each hydrologic soil type
is constant during the entire study period. In reality, the land management policy should change, with newly built areas constructed
using low impact drainage design, which can mitigate the hydrologic impacts of urbanisation (Meierdiercks et al., 2010; Ogden
et al., 2011). Therefore, the changing land management policy,
hydrologic soil type and drainage networks will be considered in
our further studies.
Nevertheless, a framework is proposed in this study which is
composed of three segments: projecting future land use using a
distributed land use change model, developing urbanization scenarios by overlaying a series of impervious surfaces to a baseline
land use map, and assessing hydrologic response of urbanization
with a distributed hydrological model. Our study demonstrates
that this is a good approach to evaluate the hydrologic impacts
of urbanization, which must be considered in watershed management, water resources planning, and flood planning for sustainable
development.
Acknowledgement
This work was supported by the National Natural Science
Foundation of China (No. 40730635) and the Priority Academic
Program Development of Jiangsu Higher Education Institutions.
The corresponding author was also supported by the Programme
of Introducing Talents of Discipline to Universities—the 111 Project
of Hohai University. The authors would like to express their great
thanks for the reviewers’ comments and suggestions which have
greatly improved the quality of the paper. Special thanks are given
to Prof. Tim Fletcher who kindly corrected the language and provided valuable comments and advice that greatly improved the
quality of the paper.
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