Journal of Hydrology 485 (2013) 113–125
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Journal of Hydrology
journal homepage: www.elsevier.com/locate/jhydrol
Hydrological response to urbanization at different spatio-temporal scales
simulated by coupling of CLUE-S and the SWAT model in the Yangtze River Delta
region
Feng Zhou a, Youpeng Xu a,⇑, Ying Chen b, C.-Y. Xu c, Yuqin Gao a, Jinkang Du a
a
b
c
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China
College of Geographic Sciences, Fujian Normal University, Fuzhou 350007, China
Department of Geosciences, University of Oslo, Norway
a r t i c l e
i n f o
Article history:
Available online 8 January 2013
Keywords:
Urbanization
Hydrological response
Spatio-temporal scale
SWAT model
s u m m a r y
The Main objective of the study is to understand and quantify the hydrological responses of land use and
land cover changes. The Yangtze River Delta is one of the most developed regions in China with the rapid
development of urbanization which serves as an excellent case study site for understanding the hydrological response to urbanization and land use change. The Xitiaoxi River basin, one of the main upstream
rivers to the Taihu Lake in the Yangtze River Delta, was selected to perform the study. The urban area in
the basin increased from 37.8 km2 in 1985 to 105 km2 in 2008. SWAT model, which makes direct use of
land cover and land use data in simulating streamflow, provides as a useful tool for performing such studies and is therefore used in this study. The results showed that (1) the expansion of urban areas had a
slight influence on the simulated annual streamflow and evapotranspiration (ET) as far as the whole
catchment is concerned; (2) surface runoff and baseflow were found more sensitive to urbanization,
which had increased by 11.3% and declined by 11.2%, respectively; (3) changes in streamflow, evapotranspiration and surface runoff were more pronounced during the wet season (from May to August),
while baseflow and lateral flow had a slight seasonal variation; (4) the model simulated peak discharge
increased 1.6–4.3% and flood volume increased 0.7–2.3% for the selected storm rainfall events at the
entire basin level, and the change rate was larger for smaller flood events than for larger events; (5) spatially, changes of hydrological fluxes were more remarkable in the suburban basin which had a relative
larger increase in urbanization than in rural sub-basins; and (6) analysis of future scenarios showed the
impacts of urbanization on hydrological fluxes would be more obvious with growth in impervious areas
from 15% to 30%. In conclusion, the urbanization would have a slight impact on annual water yield, but a
remarkable impact was found on surface runoff, peak discharge and flood volume especially in suburban
basins in the study area. The study suggested that more attention must be paid for flood mitigation and
water resources management in planning future urban development in the region.
Ó 2013 Elsevier B.V. All rights reserved.
1. Introduction
Urbanization, which typically replaces a permeable vegetated
land surface with impervious surface areas, significantly changes
the hydrologic fluxes of a drainage basin. It causes local decreases
in infiltration, canopy interception and the water holding ability of
the basin (Rose and Peters, 2001; Aronica and Cannarozzo, 2000),
and has the potential to produce huge floods (Huang et al., 2008;
Olang and Furst, 2010).
Yangtze River Delta is one of the most developed regions in
China; it only covers 1% of Chinese territory and 6% of Chinese population, brings about 17% of the Gross Domestic Product (GDP) in
⇑ Corresponding author. Tel.: +86 2583595069.
E-mail address: xuyp305@yahoo.com.cn (Y. Xu).
0022-1694/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.jhydrol.2012.12.040
China (Dong, 2004). With the dramatic economic growth, densest
population and high degrees of urbanizations, the region is facing
serious problems of flood risk, water quality deterioration and
water shortage etc., which seriously threatens the living environment of local population and restricts the sustainable development
of regional economy. To promote effective ways to rehabilitate the
regional environment and develop programs of sustainable landresources utilization, it is significant and imperative to understand
the potential consequences of urbanization on hydrologic fluxes in
this region (Xu et al., 2010; Chen et al., 2009).
There have been many studies examining the hydrological response to urbanization around the world and most results indicated the impact of urbanization on water resources is obvious
but with varying characteristics in different regions. Brun and Band
(2000) assessed the effects of urbanization on watershed behavior
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F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125
in upper Gwynns Falls from pre-urbanized times to 1990 and
found that baseflow had declined by 20%; however, Brandes et al.
(2005) suggested that increases in impervious area might not result in measurable reductions in baseflow at the watershed scale
after examining long-term streamflow records from unregulated
watersheds of the lower to middle Delaware River basin. Beighley
et al. (2003) also found urbanization was shown to increase peak
discharges and runoff volume while decrease streamflow variability in a Mediterranean climate. Jennings and Jarnagin (2002)
showed a significant increase in streamflow response to land use
and land cover change (LUCC) following a growth in impervious
areas from 3% to 33% in Virginia, USA. Similar findings were reported by Kim et al. (2002) and Beighley et al. (2003).
However, Brun and Band (2000) did not detect any significant
increase in annual runoff coefficients in an urbanizing watershed
of the Baltimore Metropolitan area of the USA with 20% increases
in urban development between 1970 and 1987. Chang (2003) also
showed no significant increase (less than 2%) in annual runoff
when land use was converted from agricultural land to low-density
suburban developments in a simulation study of a southeastern
Pennsylvania watershed. Similarly, Chang (2007) sought to compare changes in the streamflow characteristics of urbanized catchments and less-urbanized catchments using various streamflow
indices with different temporal scales, and found the influence of
urbanization on the hydrology of a region was more pronounced
at a shorter time-scale, which suggested that annual and monthly
scale analysis may not be appropriate for detecting the urban signal on hydrology.
Meanwhile, the spatial pattern of urbanization was suggested to
affect hydrological fluxes. Petchprayoon et al. (2010) found that increase in streamflow at downstream areas of the rapid urbanization was significantly greater than that at upstream areas in the
Yom watershed in central–northern Thailand, and the similar findings were reported by Old et al. (2003) and Guo et al. (2008). In
addition, Niehoff et al. (2002) showed that the influence of land
use conditions on storm runoff generation depends greatly on
the rainfall event characteristics and on the related spatial scale
in a meso-scale catchment in SW-Germany, i.e., the influence is
only relevant for convective storm events with high precipitation
intensities in contrast to long-lasting advective storm events with
low precipitation intensities.
These previous research findings suggest that impact of urbanization on hydrology depends on the spatial and temporal scales,
climate variability, and physical characteristics of the study region.
Hence, apparently, it is necessary to do more systematical investigations on the problem. As stated by Wagener (2007), the hydrological impacts of land use and land cover changes are still
contentious issues and further research is necessary. A case study
in the subtropics monsoon climate environment and rapid urbanization of the Yangtze River Delta, might improve the understanding of the past urbanization impact on hydrology processes.
Hydrological models are frequently used for quantifying the impact of land use change on hydrological components on watershed
scale (Liu et al., 2006; Jiang et al., 2012; Ren et al., 2012; Yang et al.,
2012). The principle of model selection is based on the purpose of
the study and data accessibility, and many previous studies have
demonstrated the ability of SWAT (Soil and Water Assessment
Tool) in detecting the impact of land use and/or climate changes
on hydrological variations in different catchments in many countries of the world (e.g., Jayakrishnan et al., 2005; Heuvelmans
et al., 2005; Wu and Johnston, 2007; Ma et al., 2009; Li et al.,
2009; Du et al., 2012).
In this study the SWAT model is used to examine the impacts of
land use change on hydrological fluxes in a rapid urbanization
region in the lower reach of the Yangtze River. The specific objectives of this study are to: (1) identify the historical changes of
urbanization over a period of 23 years (1985–2008); (2) understand and quantify the different characteristics of urbanization impacts on hydrological fluxes at various spatial and temporal scales;
and (3) evaluate the potential hydrological response to urbanization scenarios in the near future to provide a reference for urban
sustainable development in the study area.
2. Materials and method
2.1. Study area
Xitiaoxi basin, located in the northwest of Yangtze River Delta
between latitude of 30°230 –31°110 and longitude of 119°140 E–
120°290 E, is selected to perform the study. It covers an area of
1371 km2 at the Hengtangcun station (Fig. 1). The study area is
characterized by a subtropical climate, with an average annual
temperature and precipitation of 15.5 °C and 1465.8 mm, respectively. More than 75% of the annual precipitation falls in wet season (from April to October), while less than 25% occurs in the dry
season (from December to March). The mean annual potential
evaporation varies from 800 mm to 900 mm, with the maximum
evaporation occurs in July and August. The detailed information
on the physical characteristics of the basin (i.e. land cover and major soil types) is discussed in Section 2.4.2.
As one of the most important tributaries in the upstream of Taihu Lake basin in the Yangtze River Delta, the Xitiaoxi River supplies
26.8 108 m3 water into the Taihu Lake (27.7% of the water volume of the Lake). There are two large reservoirs (i.e., Fushi and Laoshikan reservoirs) in the upstream area of the basin, which are
primarily used for flood control in rainy season. Since the objective
of our study was to assess the urbanization effect on hydrological
fluxes, and as most of the upstream forest is native with little land
cover change, the region between the Hengtangcun station and the
two reservoirs was chosen for the detailed study (deep color region
in Fig. 1). The daily observed outflow data of the two reservoirs
were taken as the inflow to the downstream area and the daily observed streamflow data of Hengtangcun station was used for model calibration.
2.2. Historical land cover change assessment
The change of a single land type percentage (PAi) and a transition matrix (Cij) were used to assess the internal conversion of different land cover types, and the two variables are given as follows:
PAi ¼ ðAkþ1
Aki Þ=Aki
i
2
c11 c12 c13 c14
ð1Þ
3
6 c21 c22 c23 c24 7
6
7
C ij ¼ 6
7
4 c31 c32 c33 c34 5
ð2Þ
c41 c42 c43 c44
where Aki and Akþ1
are the areas of land cover type i in periods k and
i
k + 1, respectively; Cij denotes the area of land type i in period k converted to land type j in period k + 1, which was created by spatial
analyst toolbox available with ESRI ArcGIS and regular spreadsheet
software (Pang et al., 2010).
2.3. Future urbanization scenarios establishment
The CLUE-S model (the Conversion of Land Use and its Effect at
Small regional extent) was coupled with the SWAT model to simulate the effects of future urbanization scenario based on historical
land use change tendency. The CLUE-S model was developed to
simulate land use change using empirically quantified relations
between land use and its driving factors in combination with
F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125
115
Fig. 1. Location map and observed sites of the basin.
dynamic modeling of competition between land use types (Verburg et al., 2002). The CLUE-S model includes three modules: gross
demand control module, spatial analysis module and spatial allocation module, assuming that the relationship between land use
process and driving force stays unchanged in the near future. This
approach can predict and simulate the spatial expression of land
use changes in the near future by the regression relationship between historical land use patterns and the driving force.
The CLUE-S model has been widely used in international researches and many scholars have conducted simulation by controlling different scenario settings and the total demand for the
corresponding land patterns. In this study, the Markov Chain was
used to predict future urbanization scenarios based on the transition matrix from 2002 to 2008 (Muller and Middleton, 1994).
The spatial land use change was simulated with the CLUE-S model
based on the land suitability factors of elevation, slope, aspect, soil
type, distance to provincial road, distance to river, and distance to
town.
The accuracy assessment of model results was based on the
Kappa coefficient which is used to compare the reference map with
the simulated map or to compare two reference maps. The collection known as Kappa coefficient comes from the notion initiated by
Scott (1955) that the observed cases of agreement between two
maps include some cases for which the agreement was by chance
alone, and the form of the definition of a Kappa coefficient is as
follows:
Kappa ¼ ðP o Pc Þ=ðPp Pc Þ
ð3Þ
where Po is the observed proportion correct, Pc is the expected proportion correct due to chance, and Pp is the proportion correct with
perfect match between two maps, if the agreement between two
maps is perfect, then Kappa = 1. The simulated land cover map of
2008 was compared to the actual land use map in 2008; the resultant kappa coefficient was 0.82, which means that the calibrated
CLUE-S model can be used to simulate future land cover in the study
area (Batisani and Yarnal, 2009).
2.4. Model setup and calibration
2.4.1. SWAT model
SWAT is a continuous and spatially distributed model designed
to predict the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use and management conditions
over long periods of time (Neitsch et al., 2002). In the application
of SWAT model, the study basin is first subdivided into subbasins
based on DEM (digital elevation model) and channel network,
and further delineated by smaller modeling units, known as hydrologic response units (HRUs) according to topography, types of landuse and soil. Routing of water is simulated from the HRUs to the
subbasin level, and then through the stream network to the basin
outlet.
Flow routing through channel system to the gauges (i.e. streamflow) mainly consists of surface runoff (Qsurf), lateral flow from
unsaturated soil profiles (Qlatf) and baseflow from underground
storage (Qgw). A kinematic storage model which accounts for variation in conductivity, slope and soil water content, is used to predict lateral flow in each soil layer. The model was developed by
Sloan et al. (1983) and summarized by Sloan and Moore (1984)
as below:
Q latf ¼ 0:024 2 SW ly;excess K sat slp
Ud Lhill
ð4Þ
where SWly,excess is the drainable volume of water stored in the saturated zone of the hillslope per unit area (mm); Ksat is saturated
hydraulic conductivity (mm/h); Lhill is the hillslope length (m); slp
is slope of the land; and Ud is drainable porosity of the soil layer.
SWAT differentiates the underground storage into two portions,
shallow aquifer and deep aquifer. The shallow aquifer receives recharge, i.e., percolation from the unsaturated soil profile. An exponential decay weighting function is utilized to account for the time
delay in aquifer recharge once the water exits the soil profile, while
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F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125
it assumes that water entering the deep aquifer is not considered
in the future water budget calculations and can be considered as
lost from the system. The baseflow (i.e., groundwater in the SWAT
output) was generated from the shallow aquifer as following:
Q gw;i ¼ Q gw;i 1 expðagw DtÞ þ W rchrg;i ½1 expðagw DtÞ
ð5Þ
where Qgw,i is baseflow from the shallow aquifer on day i (mm/day);
agw is the baseflow recession constant; D t is the time step length;
Wrchrg,i is the amount of recharge entering the shallow aquifer on
day i (mm/day).
In the present study, surface runoff was predicted from daily
rainfall by the Soil Conservation Service (SCS) curve number method based on land use and soil data in the study area. The Muskingum method was used for channel flow routing, and the Penman–
Monteith method was selected to calculate potential evapotranspiration. A more detailed description of the model and its underlying
conceptualizations and parameters refer to the SWAT Technical
Manual (Neitsch et al., 2002). The basin was delineated into 15
sub-basins with a threshold area of 1100 ha based on the digital
elevation model (DEM) (Fig. 1).
2.4.2. Data preparation
Thematic maps required by the model included digital elevation
model (DEM), soil types and properties data, land use and land cover map of 1985, 2002 and 2008, and the observed hydro-meteorological data, which are described as follows.
(1) Land cover map. The land cover maps of the study area were
acquired by digitizing the land use maps (1:100,000) in 1985
and 2002 (Fig. 2), and were reclassified into four categories
including forest, cropland, urban and water bodies. Land
cover in 2008 was from Landsat TM by supervised classification and manual interpretation after radiometric and geometric correction. The overall accuracy of land cover
classification was about 89.5% and the overall Kappa coefficient was 0.86, which showed an acceptable precision. The
land cover and plant growth parameters used in the model
were estimated using default values in the SWAT user
manual.
(2) DEM. The DEM of the basin was derived from 1:10,000 topographic contour data and then resized to 30 m 30 m resolution for the model input.
(3) Soil. The SWAT model requires different soil properties
including soil texture, available water content, hydraulic
conductivity, bulk density and organic carbon content for
the different layers of each soil type. Spatial soil data with
the resolution of 1:100,000 were obtained from national soil
survey data map provided by the Anji Bureau of Agriculture,
which included yellow soil, red soil, alluvial soil, yellow–red
soil, paddy soils, endodynamorphic soil and eroded red soil.
The dominate soil type is yellow–red soil which accounts for
49.21% of the study area follows by paddy soil and yellow
soil which account for 21.18% and 11.53%, respectively.
Contents of sand, clay and silt for soil layers were converted
according to SWAT criteria by cubic spline interpolation (Cai
et al., 2003). Soil stratification, soil depths, soil organic matter
and maximum rooting depth were from the soil survey report. Bulk
density and saturated hydraulic conductivity were calculated by
the SPAW model (Soil–Plant–Atmosphere–Water, Saxton and Willey, 2006) developed by Agricultural Research Service, USA. Soil
hydrological grouping was carried out according to soil texture
and hydraulic conductivity, and the above soil components and
properties were appended to the SWAT model database.
(4) Hydro-meteorological data. The hydro-meteorological data
were provided by Anji Department of Hydrology and Meteorology including daily runoff, rainfall, temperature, maximum temperature, minimum temperature, wind speed and
relative humidity data from 1972 to 2009, and the hydrological database has been scrutinized on a routine basis before
publication. Nine daily observed rain storms with different
magnitude peak discharges and peak feature (single or
multi-peak) were selected to assess the effect of urbanization on storm runoff events over various land cover scenarios. Annual streamflow analyzed in this paper was from the
Hengtangcun hydrological station (the outlet of the
watershed), and the areal rainfall was calculated by Thiessen
Polygon method from neighboring gauges located in the
study area. The location of the hydrological stations, rainfall
gauges and weather stations are shown in Fig. 1.
2.4.3. Model calibration and validation
Hydro-meteorological data of 1983–1987 and land-cover map
of 1985 were used to calibrate model parameters; the land-cover
map of 2002 with climate data of 1999–2004 and land-cover
map of 2008 with climate data of 2007–2009 were used for model
validation.
In consideration of difficulties in the measurement of baseflow,
the digital filter-based program was widely used for model calibration (Luo et al., 2012; Yang et al., 2003). In the present study, an
Fig. 2. Land use and land cover map of Xitiaoxi basin (1985–2008).
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F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125
automatic baseflow filter program was used to separate baseflow
and direct runoff (the total amount of surface runoff and lateral
flow) from the daily streamflow records (Arnold et al., 1995,
http://swat.tamu.edu/software/baseflow-filter-program/), and the
total runoff was firstly calibrated, then for direct runoff and baseflow calibration in order of yearly, monthly and daily basis until
acceptable results were obtained.
Through One-factor-At-a-Time (LH-OAT) global sensitivity analysis procedure embedded in SWAT model (Griensven and Meixner,
2006), the seven most sensitive parameters of the SWAT model
were identified including CN2 (Curve Number), SOL_AWC (water
capacity of soil layers), ESCO (soil evaporation compensation factor), GWQMN (threshold depth of water required in a shallow
aquifer for return flow to occur), Alpha_BF (baseflow alpha factor),
SURLAG (surface runoff lag time) and SOL_K (saturated hydraulic
conductivity). Nash–Sutcliffe efficiency (Ens) and Correlation coefficient (R) were used to evaluate the performance of the model.
When Ens approaches 1.0, the model simulates the measured data
perfectly, and when Ens is negative the model is a worse predictor
than the measured mean value. The equation for Ens is as follows:
n
X
Ens ¼ 1:0 ðQ si Q oi Þ2
i¼1
,
n
X
ðQ si Q o Þ2
ð6Þ
Dv ¼
N
N
X
X
Q si Q oi
i¼1
!,
N
X
Q oi
i¼1
ð7Þ
i¼1
Dp ¼ ðQ sp Q op Þ=Q op
ð8Þ
DT ¼ T sp T op
ð9Þ
where Qsp and Qop are the peak discharges of the simulated and observed hydrographs over the simulation period, Tsp and Top are the
time for the observed and simulated hydrograph peaks to arrive,
and N is the total number of time steps.
A comparison of the simulated and observed runoff hydrographs using the above four criteria was shown in Table 2. Over
the calibration period, the Ens values are ranging from 0.78 to
0.96 with an average value of 0.88. The Dp has an average absolute
value of 12.7 and Dv has an average absolute value of 7.9. The calibrated parameters were validated for the other five historical rainstorm events, and the model performance was found acceptable.
The calibration and validation of the model for different time
steps (i.e. annual, monthly, daily and storm runoff events) provide
acceptable results, therefore, the model is capable of predicting
streamflow in different land cover scenarios in the study basin.
i¼1
where Qoi and Qsi are the ith observed and simulated streamflows at
time i, Q o is the mean observed data over the simulation period, and
n is the total number of observations.
The annual and monthly calibration results of the model were
shown in Table 1 and Fig. 3. For the calibration period of 1983–
1987 at monthly level, Ens = 0.97 and R = 0.98; for the validation
period of 1999–2004, Ens = 0.95 and R = 0.97; and for the validation period of 2007–2009, Ens = 0.94 and R = 0.97. Model performance for the daily calibration and validation period were less
favorable but still reasonable with average Ens = 0.86 and R = 0.92.
The calibrated and validated hydrographs of nine historical
rainstorm events with various magnitude and process were shown
in Fig. 4, where four events were selected for calibration and other
events were used for validation. Four evaluation criteria are used in
model evaluation including Ens, the deviation of flood volumes (Dv,
the total amount water in the flood), the deviation of peak discharge (Dp) and error of time to peak (4T). The equations for Dv,
Dp and 4T are as follows:
3. Results and discussion
3.1. Land cover change
The areas of different land cover and their changes are listed in
Table 4 and illustrated in Fig. 2, and the conversion matrices of
land-use changes in total area are summarized in Fig. 5. The land
cover in the study area can be categorized into four types, i.e. forest
land (including small portion of grass), cropland, urban land and
water body. Spatially, the massif area in the upper reaches is
mainly covered by forest, the hilly area in the middle reaches by
forest and cropland, while the plain area in the lower reaches by
urban and cropland (Fig. 2).
From 1985 to 2002, land cover changes can be summarized as
increases in urban areas by 80.6%, but decreases in forest and cropland by 2.1% and 11.9%, respectively. The total conversion area accounted for 17.3% of the entire area, and the predominant trend
was conversion of cropland to forest and forest to cropland, which
Table 1
Evaluation criteria for discharge simulation during calibration and validation periods.
Annual average value
Calibration (1983–1987)
Validation (1999–2004)
Validation (2007–2009)
Monthly average value
Daily value
R
Ens
R
Ens
R
Ens
0.97
0.99
0.99
0.92
0.99
0.98
0.98
0.97
0.97
0.97
0.95
0.94
0.93
0.92
0.90
0.95
0.84
0.79
Fig. 3. Comparison of simulated and observed monthly streamflow in the calibration period (1983–1988) and validation periods (1999–2004 and 2007–2009).
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F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125
Fig. 4. Comparison of observed and simulated hydrographs for the selected nine storm rainfall events.
Table 2
Evaluation criteria for the selected nine rainfall events during calibration and validation periods.
Period
Land use scenarios
Flood no.
Calibration
1985
198406
417.2
198409
218.7
198604
80.4
199009
309.0
Average absolute value
Validation
2002
199906
744.2
200108
236.3
200710
191.1
200806
412.4
200908
409.8
Average absolute value
2008
Observed flood volume (mm)
Observed peak discharge (m3/s)
Dv (%)
Dp (%)
Ens
4T
1130
611
240
1070
3.7
3.4
1.6
22.7
7.9
8.1
8.9
21.8
12.1
12.7
0.94
0.96
0.78
0.82
0.88
0
0
0
0
0
807
348
888
700
748
8.3
8.3
13.7
9.6
26.2
13.2
8.9
6.5
2.1
7.5
2.2
5.4
0.89
0.89
0.92
0.81
0.83
0.89
0
0
0
0
0
0
Table 3
Characteristics of land use changes in Xitiaoxi watershed from 1985 to 2008.
Area (km2)
Land Use
Percent (%)
Change rate (%)
Class number
Name
1985
2002
2008
1985
2002
2008
1985–2002
2002–2008
1985–2008
1
2
3
4
Forest
Cropland
Urban
Water body
505.6
168.0
37.8
9.0
495.1
147.9
68.3
9.2
475.6
133.9
105.0
5.9
70.2
23.3
5.2
1.3
68.7
20.5
9.5
1.3
66.0
18.6
14.6
0.8
2.1
11.9
+80.6
+1.4
3.9
9.4
+53.8
36.0
5.9
20.3
+177.8
35.1
Table 4
Variation in annual runoff depth (mm) over different land use scenarios in different hydrological years.
Hydrological
years
Wet (1983)
Average (2002)
Dry (2006)
Simulated year runoff depth in 1985 Scenario
(mm)
1217
815
415
Percentage increase (%)
1985–2002
2002–2008
2008-Scenario
20
Scenario 20–Scenario
25
Scenario 25–Scenario
30
0.3
0.8
3.1
0.5
0.6
2.3
1.3
2.2
5.0
1.8
1.5
3.5
0.4
1.4
3.2
1985–2002 Means the percentage increase from 1985 to 2002 scenarios.
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F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125
Fig. 5. Percentage of land use change types in total area and selected sub-basins from 1985 to 2008. (The numbers 1, 2, 3, 4, represent the forest, cropland, urban and water,
respectively; e.g. 12 means transition from land use 1 to land use 2).
accounts for 4.9% and 5.9% of the entire area, respectively; and the
rest is the conversion of forest and cropland to urban area.
From 2002 to 2008, the region had witnessed the most rapid urban development, and the most noticeable changes in land use was
conversion of cropland and forest to urban with the total conversion area accounted for 11.6% of entire area. In terms of the change
rate, the urban has undergone the biggest changes by 53.8% (from
68.3 km2 in 2002 to 105.0 km2 in 2008), following by water body
with a decreasing rate of 36.0%, agriculture by 9.4% and forest
by 3.9%, respectively (Table 3).
Comparison of the land cover maps for 1985 and 2008 reveals
that the urban part of the catchment increased for about 10% in
the study period. The spawn of the urban was more obvious after
2002 with an average annual growth rate of 0.85%, which was
about three times of the previous period. Spatially, the change
was more drastic in the lower reaches close by the city, and the
changes were mainly caused by the rapid urbanization with the increase in population. In general, the change can be summarized as
increases in urban area and decreases in agriculture and forest.
There are four dominating conversion types, i.e. from cropland to
urban, from forest to urban, and mutual conversion of the forest
and cropland. Area of land cover change type was shown in
Fig. 5, and it illustrated that the increasing in urban area was
mainly gained from the decreases in cropland, which represented
the regional urbanization development pattern.
The hydrological response to historical change in land cover
especially urbanization and its impacts at sub-basin level will be
discussed in Section 3.3. For spatial analysis, the whole area was
divided into 15 sub-basins by ArcSAWT, and three typical sub-basins (S4, S7 and S15) were selected (Figs. 1 and 5), representing different conversion patterns and urbanization degrees. The
description of the sub-basins is as following:
(1) S4: The sub-basin is a typical suburban basin which
accounts for 7.5% of the study area, and the basin had undergone a drastic urbanization from 8.0% in 1985 to 39.3% in
2008. The change area accounted for 43% of the sub-basin
area, and the conversion area of cropland to urban and forest
to urban accounts for 25.1%, and 5.7% respectively. The conversion area of the cropland to forest and forest to cropland
were nearly the same which accounted for 5.8% and 5.2%,
respectively, and the remaining conversion proportion was
less than 2%.
(2) S15 and S7: The sub-basin 15 (S15) was selected as a basin
in natural condition which accounts for 5.3% of the study
area, and the total conversion area accounts only around
3% of the sub-basin area. The sub-basin 7 (S7) accounts for
8.9% of the study area, and the sub-basin was selected for
moderate urban development condition; the increase in
urban area was from 4.3% in 1985 to 8.5% in 2008 which
was gained from cropland. The total area of the forest had
nearly no change.
3.2. Characteristics and long-term change of streamflow, rainfall and
temperature
The coefficient of variation of annual runoff (Cv) and annual extreme ratio (Ae = maximum value/minimum value) are used to assess the annual streamflow variations. The calculated Cv and Ae is
0.34 and 3.94, respectively, which indicates that there was an obvious interannual variation in the annual runoff. We also grouped
wet and dry years based on interannual variations of streamflow
as follows (Hu, 2000):
Q
100%
P ¼ ðQ i QÞ=
ð10Þ
is
where P is anomaly percentage, Qi is annual runoff in year i, and Q
the average of annual runoff. When P > 20%, it is a wet year,
20% < P 6 20% is an average year and P < 20% is a dry year.
Fig. 6. Wet/average/dry distribution of Hengtangcun station (1972–2009).
120
F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125
Fig. 7. Annual hydrological fluxes variations in different land use conditions from 1985 to 2008. Bar graph shows the annual values in the typical year, and lines show the
change rates for the specified period.
During 1972–2009, the wet year period was short (only 1–3 years),
while the dry year period usually lasted longer with maximum value of 5 years. The basin was mainly in the wet period during the
1980s, and in dry period after 2000s (Fig. 6). This result is used to
investigate the difference in effects of urbanization on hydrological
fluxes in different hydrological years as discussed in details in
Section 3.3.2.
The results of simple linear regression also indicated a slight decrease in the annual average streamflow series by 0.1 m3/s every
year, a decrease in rainfall by 3.3 mm/yr and an increase in temperature by 0.05 °C/yr over the period 1972–2009. The distribution
of annual total streamflow in the study area was mainly controlled
by rainfall, which is dominated by the Asian summer monsoon,
and the result revealed a high positive correlation coefficient
(R = 0.84) between the annual streamflow and rainfall, and a slight
negative correlation between the streamflow and temperature
(R = 0.47). While the annual runoff coefficient shows nearly no
change by 0.003/yr over the period, it might due to the fact the
rainfall played a dominated role in the variation of streamflow as
far as the whole basin is concerned, as well as the uncertainty in
spatial distribution of rainfall and areal rainfall calculation by Thiessen Polygon method. The slight decreasing trend in runoff coefficient might be attributed to that the basin was mainly in dry
period after 2000. However, the historical land cover change analysis conducted in Section 3.1 showed that the region also had
undergone significant urbanization, and the impact of urbanization
on storm runoff events and on hydrological fluxes at different spatial and temporal scales will be examined in the following sections.
attributed to the fact that ET decreased with lower infiltration rate
and soil moisture storage capacity due to the increase in impervious surface.
In terms of monthly variations of hydrological fluxes, the
change in streamflow was more pronounced during the wet season
(from May to August), while a relative smaller change in dry season. For example, there was an increasing rate of 2.5% in June
and nearly no change in November (Fig. 8). The change in ET was
similar with change trend of streamflow but with different sign,
and it has a decrease by 2.5% in June. In terms of surface runoff,
there was a higher increase in wet seasons (from May to August),
and the largest increase was found in June. The seasonal variation
in baseflow and lateral flow was small.
3.3. Impacts of urbanization on long-term hydrologic components
3.3.3. The relationship of hydrologic response and land use change at
sub-basin level
The spatial impacts of LUCC at sub-basin level were analyzed by
the correlation analysis of urbanization and change of hydrological
fluxes in 15 subbasins over the periods 1985–2002 and 2002–
2008. The results indicated that urbanization had a high correlation with the streamflow, surface runoff and baseflow, with the
correlation coefficient (R) of 0.95, 0.84 and 0.89, respectively,
while the urbanization rate had a relatively lower correlation with
ET (R = 0.49) and lateral flow (R = 0.20). The changing rate in the
hydrological fluxes at subbasin level was coincided with changing
rate of urbanization. For the selected sub-basins, the subbasin S4
had the largest urbanization rate (from 8.0% in 1985 to 39.3% in
2008) caused the largest change in the hydrologic components,
where the annual surface runoff increased by 23.2%, the baseflow
decreased by 30.3%, total streamflow increased by 5.7% and ET decreased by 2.4%, respectively. The selected subbasin S7 had a small
3.3.1. Hydrological impact on annual and monthly streamflow at the
entire basin level
Based on the SWAT model, the long-term hydrological response
to the land cover change was investigated by fixing the climatic
conditions and changing the land cover scenarios during the period
1983–2009. The change rate in annual streamflow, surface runoff
and evapotranspiration (ET), lateral flow and baseflow were shown
in Fig. 7. It illustrated that the annual streamflow and surface runoff increased, while the ET, lateral flow and baseflow decreased due
to the land cover change. Surface runoff and baseflow were found
more sensitive to land cover change than total streamflow, lateral
flow and ET. The effects of land cover change from 1985 to 2008
would increase surface runoff by 11.3% and decrease baseflow by
11.2%, while the change rates in streamflow and ET were within
2%. The increase in annual streamflow and surface runoff might be
3.3.2. Changes of hydrological fluxes in various hydrological conditions
In order to investigate the changes of hydrological fluxes in different hydrological years, 2002 (P = 50%), 1983 (P = 90%) and 2006
(P = 10%) were chosen to represent average, wet and dry years,
respectively. Fig. 9 shows the effects of land use change on hydrological fluxes differed in different hydrological years. The annual
increase (decrease) in surface runoff (baseflow) was much higher
in wet years than that in dry or average years, while ET had a larger
decrease in dry years. The increase of annual streamflow in the dry
years was much higher than that in the average or wet year. In the
dry year, land use and land cover change (LUCC) from 1985 to 2008
increased the annual total runoff depth by 23 mm (5.5%); in the
average year by 12 mm (1.4%), while in the wet year by 9 mm
(0.8%).
F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125
121
Fig. 8. Monthly changes in hydrological fluxes due to land cover change from 1985 to 2008.
Fig. 9. Box-plot of annual variations of hydrologic fluxes in different hydrological years.
increase in urban area from 4.3% in 1985 to 8.5% in 2008 which was
mainly gained from the cropland, the change in surface runoff and
baseflow was within 10%, and the change rates in total streamflow
and ET were less than 2%. While the nature basin S15 (with almost
no land use change) had a subtle change in all hydrologic
components.
The spatial distribution of the changes in hydrological fluxes
was shown in Fig. 10, where remarkable spatial variations of the
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F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125
Fig. 10. The percentage increase in total runoff depth and urbanization in 2008 relative to 1985 for different hydrological years: (A) the increase in urban area, (B) wet year,
(C) average year, (D) Dry year) at the subbasin level.
hydrological fluxes can be detected. The figure indicated that the
hydrological impacts of land use change would be most pronounced in suburban areas in dry years.
3.4. Effect of urbanization on storm runoff events
In order to investigate the different impacts of urbanization on
storm runoff events with various magnitudes, the selected nine
storm runoff events were ranked in ascending order in Fig. 11. It
was found that urbanization increased the flood peak discharge
and volume for all the storm runoff events, and the change rates
are dependent on the magnitude of the storm runoff events. The
change in land cover from 1985 and 2008 increased the flood volume (by 0.7–2.3%) and the peak discharge (by 1.6–4.3%), and the
change was more distinct for peak discharge than for flood volume.
A closer look at the figure revealed that the change rate was related
to antecedent 15-day precipitation (A15d) and the sensitivity of
hydrologic response to urbanization tended to increase as the
recurrence interval of the rainfall events decreases, i.e., it is more
pronounced for small flood events. For example, the two smaller
flood events (i.e. Nos. 198604 and 200108) had higher increase
rates of 4.3% and 3.6% for peak discharge than the other larger flood
events.
For multi-peak flood events, the former rainfall event brought
more soil moisture and higher groundwater levels which would
weaken the urbanization impact, e.g., the flood No. 200806 which
has two flood peaks had a larger effect on the former peak discharge with an increase rate of 4.2% comparing to the latter peak
discharge with an increase rate of 2.6%. The same is true for flood
No. 199906. The results suggested that antecedent soil moisture
conditions and groundwater levels played a certain role for the degree to which urbanization might influence storm runoff
generation.
The impacts of distinct changing rates of urban expansion
(Fig. 10A) on the subbasins hydrological fluxes are shown in
Fig. 12, which indicated that hydrological impacts would be most
pronounced in suburban basin. Taking the selected subbasins as
an example, the suburban basin (S4) had a highest increment in
urban land use from 1985 to 2008, and the increases in peak
discharge and flood volume were from 5.1% to 10.6% and from
4.9% to 10.1%, respectively. While subbasin 7 (S7) showed a small
increase in peak discharge and flood volume by around 3%. The
F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125
123
Fig. 11. Percentage increase in peak discharge and flood volume over the land cover change period (A15d is antecedent 15-day precipitation; 1985–2002 means the change
from 1985 to 2002 scenarios).
Fig. 12. Percentage increases of peak discharge and flood volume in 2008 relative to 1985 for flood No. 198406 at the subbasin level.
subbasin with natural condition (S15) showed nearly no change in
flood volume and peak discharge. Comparison of the impact between the storm events and annual streamflow analyses (Figs. 10
and 12) shows the different impact at subcatchment level, which
might be attributed to the fact the uncertainty in spatial distribution of rainfall would have more impact on the assessment of
annual hydrological process than for storm event.
3.5. Hydrological impacts of future urbanization scenarios
The analysis of the historical land cover change showed the percentage of the urban area was around 5%, 10% and 15% in 1985,
2002, and 2008 respectively. To further study the urbanization
impact and provide a reference for urbanization development in
the near future, the calibrated CLUE-S model was executed for
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F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125
Fig. 13. Variation of peak discharge (m3/s) and flood volume (mm) for Flood 198406 (20-year flood) and flood 200710 (10-year flood).
the following three scenarios with an urbanization rate of 20%
(Scenario 20), 25% (Scenario 25) and 30% (Scenario 30), respectively. The corresponding changes of hydrological fluxes due to
the urban expansion were summarized in the bar-diagram of
Fig. 13 and Table 4, which showed that, the higher urbanization
would be able to generate more runoff. For the average year, land
use change from 1985 to Scenario 30 tended to increase the annual
runoff depth by 55 mm (6.7%), and the largest increase occurred
over the period from 2008 to Scenario 20, followed by the period
from Scenario 20 to Scenario 25; and similar result was found in
wet or dry years. The future urbanization would cause a greater
flood risk. Taking the 20–year flood as an example (Flood 198406
in Fig. 13), change in land use from 1985 to Scenario 30 would increase peak discharge by 70.7 m3/s (7.2%) and flood volume by
8.8 mm (2.4%), and the largest increase in peak discharge was
found over the period from Scenario 20 to Scenario 25.
Generally, the modeling results showed the impacts of urbanization on runoff would be obvious when the percentage of urban
area changes from 15% to 30% under the current land management
policy; which suggested that more attention needs to be paid on
river basin management and flood control in future urban development in the study area.
Impacts of urbanization on hydrological fluxes were different in
different seasons and hydrological years. The urbanization had
less impact on annual total runoff than on surface runoff and
baseflow. The increase in surface runoff caused by urban expansion was higher in wet years or in the wet seasons than in dry
years or dry seasons.
As for the storm runoff events, the urbanization increased peak
discharge more than that of flood volume, and the change rate
was found higher for small flood events than for large events.
As for multi-peak flood events, urbanization caused more
increase on former flood peak than on the latter flood peaks.
Urbanization and its impacts on hydrological fluxes can be better
understood at the sub-basin level. Suburban area might experience high flood risk as urbanization develops even though the
impact at the whole basin level might be not remarkable.
The coupling of CLUE-S and the SWAT model was found to be a
good approach in quantitatively evaluating the future urbanization impact on hydrological fluxes.
The study approach will provide useful information and reference for similar studies to be conducted in other regions, and the
results will have useful implications for flood mitigation and water
resources planning and management in the region.
4. Conclusions
Acknowledgments
To investigate the impacts of rapid urbanization on hydrological
fluxes in the Yangtze River Delta, this paper conducted the case
study in Xitiaoxi basin in the lower reach of the Yangtze River by
utilizing GIS, RS and SWAT model. The following conclusions can
be drawn.
This research is supported by the key program of National Natural
Science Foundation of China (Grant No. 40730635), the Commonweal
and Specialized Programs for Scientific Research, Ministry of Water
Resources of China (Grant Nos. 201201072, 201301075).
F. Zhou et al. / Journal of Hydrology 485 (2013) 113–125
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