Water Resour Manage (2013) 27:2823–2838
DOI 10.1007/s11269-013-0317-0
Hydrological Simulation by SWAT Model with Fixed
and Varied Parameterization Approaches Under Land
Use Change
Jinkang Du & Hanyi Rui & Tianhui Zuo & Qian Li &
Dapeng Zheng & Ailing Chen & Youpeng Xu & C.-Y. Xu
Received: 30 October 2012 / Accepted: 19 February 2013 /
Published online: 2 March 2013
# Springer Science+Business Media Dordrecht 2013
Abstract Land use and land cover (LULC) change within a watershed is recognized as an
important factor affecting hydrological processes and water resources. Modeling the hydrological effects of land-use change is important not only for after-the-fact analyses, but also
for understanding and predicting the potential hydrological consequences of existing landuse practices. The main aim of the study is to understand and quantify the hydrological
processes in a rapid urbanization region. The SWAT model and the Qinhuai River basin, one
of the most rapidly urbanizing regions in China were selected to perform the study. In the
study, a varied parameterization strategy was developed by establishing regression equations
with selected SWAT parameters as dependent variables and catchment impermeable area as
independent variable. The performance of the newly developed varied parameterization
approach was compared with the conventional fixed parameterization approach in simulating the hydrological processes under LULC changes. The results showed that the model
simulation with varied parameterization approach has a large improvement over the conventional fixed parameterization approach in terms of both long-term water balance and
flood events simulations. The proposed modeling approach could provide an essential
reference for the study of assessing the impact of LULC changes on hydrology in other
regions.
J. Du : Q. Li : D. Zheng : A. Chen : Y. Xu
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China
H. Rui
Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection,
Nanjing 210042, China
T. Zuo
Earthquake Administration of Guangxi Autonomous Region, Nanning 530022, China
C.-Y. Xu
Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern NO-0316, Oslo, Norway
C.-Y. Xu (*)
Department of Hydrology and Water Resources, Wuhan University, Wuhan, China
e-mail: chongyu.xu@geo.uio.no
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J. Du et al.
Keywords SWAT . Hydrological modeling . Land use change . Sensitivity analysis
1 Introduction
The landscape is continually changing under the influence of several factors such as
demographic trends, climatic variability, national policies, and macroeconomic activities
throughout the world during the past decades. Land cover changes within a watershed are
also recognized as an important factor affecting hydrological processes and water resources
(Schulze 2000; Stohlgren et al. 1998; Jiang et al. 2012; Li et al. 2009; Bulygina et al. 2013).
Physical understanding of the interactions between hydrology and land-use change is
important not only for after-the-fact analyses, but also for predicting the potential
hydrological consequences of existing land-use practices. Field experiment and modeling
study are main approaches to explore the interaction between hydrology and land-use
change. Field experiments can conclusively demonstrate the consequences of land use
change, while modeling studies often provide more insight into the mechanisms (Li et al.
2007). Physically-based and spatially-distributed hydrological models are not only able
to account for spatial variability of hydrological processes, but enable computation of
internal fluxes and state variables. Therefore, such models are increasingly used to
address the hydrological impacts of land-use changes (e.g., Karvonen et al. 1999; Choi
et al. 2003; Bathurst et al. 2004; Krysanova et al. 2005; Lin et al. 2009; Im et al. 2009;
Yang et al. 2012; Ren et al. 2012).
The SWAT model, a comprehensive model designed to simulate hydrology and water
quality in basins of almost any size and complexity for a long-term and continuous process,
has been widely used to simulate and predict the impact of land use changes on catchment
hydrology. For example, Franczyk and Chang (2009) used 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. Palamuleni et al. (2011) investigated the effects of the derived land cover
changes on river flow in the Upper Shire river (Malawi) using SWAT model. Pisinaras et al.
(2010) applied SWAT2005 to Kosynthos River watershed located in Northeastern Greece to
test the effect of several land use change and crop management scenarios in runoff and
nutrient loadings. Stehr et al. (2008) used SWAT to simulate hydrological processes of the
Vergara River basin, one of the sub-basins of the Biobío River basin in central Chile, for
further making a preliminary assessment of the potential impacts of land-use and climate
changes on basin hydrology. Cao et al. (2009) applied SWAT to evaluate the impacts of land
cover change on total water yields, groundwater flow, and quick flow in the Motueka River
catchment, New Zealand. Li et al. (2010) used SWAT to simulate the soil moisture in
Shaanxi Province, China, a region with complex topography. Guo et al. (2008) used the
SWAT model to examine the climate and land-use and land-cover change effects on annual
and seasonal streamflow in the Xinjiang River basin of the Poyang Lake, China. Zhang et al.
(2012) investigated the impacts of climate change and human activities on the runoff for
Huifa River basin, Northeast China using SWAT. Wang et al. (2010) modified the wetland
module in SWAT to analyze the wetland restoration potential for QingDianWa depression,
near Tianjin city, China. Kaini et al. (2012) coupled optimization technique of genetic
algorithm with SWAT to find an optimum combination of structural best management
practices for the Silver Creek, a sub-watershed of the Lower Kaskaskia watershed in
Illinois. Tuppad et al. (2010) used SWAT to simulate various best management practices
SWAT Model with Fixed and Varied Parameterization Strategies
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and assessed their long-term impacts on sediment and nutrient loads at different spatial
levels for the Bosque River watershed in Texas, USA.
In SWAT, the modified SCS-CN method is used to calculate the direct runoff generation,
once the land use changes the CN changes accordingly,leading to the change of estimated
direct runoff. In such a way the hydrological effects of land use change can be assessed. The
performance of SWAT is often judged by a simple split-sample test using historical discharge
series and land use patterns. The derived parameter values are then assumed to be identical
for the new land use scenarios except CN, which will change with new land use scenarios.
However, land use change can alter not only direct runoff, but also evapotranspiration,
velocities of overland flow and channel flow and so on. Therefore, the other parameters
associated with these processes should also be changed with new land use scenarios.
Unfortunately, no study, to the knowledge of the authors, adopts such a varied parameterization strategy when applying SWAT to simulate the hydrologic response to land use
changes. In such an approach, the key point is to establish the relationship between the
selected model parameters of SWAT (as dependent variable) and land-use change data (as
independent variables), so that the hydrological impacts of land use change can better be
simulated with varying parameters according to land use changes.
With above consideration in mind, the main goal of this study is to propose a varied
parameterization approach in using SWAT to simulate the hydrological processes under land
use changes and compare it with conventional fixed parameterization approach. The Qinhuai
River basin, an important urbanization watershed in China, was selected to exemplify the
modeling approach. The main goal is pursued by focusing on the following specific
objectives: (1) testing the capabilities of the SWAT model for long term simulation of
streamflow in the Qinhuai basin with two outlets; (2) establishing the relationships between
the model parameters in SWAT and land-use change data; and (3) comparing the model
performance with varied and fixed parameterization approaches and improving the understanding of the physical hydrological processes occurring on the watershed with urbanization condition.
2 Materials and Methods
2.1 Study Area
The Qinhuai River basin is located between 118°39′ and 119°19′ E longitude and 31°34′ and
32°10′ N latitude. It has an area of 2,631 km2, and the elevation ranges from 0 m to 417 m
above mean sea level. The basin has experienced dramatic urbanization and economic
growth over the past decades, resulting in extensive land use changes. Therefore, it is an
excellent site for the study of assessing hydrologic response to land use changes.
The studied basin lies in the humid climatic region. The annual mean precipitation is
approximately 1,047 mm, and the rainy season extends from April to September, with
intense precipitations in summer. The annual mean 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 main land use types. The main soil
types are yellow-brown soil, purple soil, limestone soil, paddy soil, and gray fluvoaqvic soil.
There are seven raingage stations within or close to the watershed and two stream flow
gauging stations at the outlets of the basin. The watershed location, elevation, distribution of
rainfall and flow gauging stations, and streams are seen in Fig. 1.
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J. Du et al.
Fig. 1 Map of Qinhuai River basin used in this study and locations of meteorological and hydrological
stations
2.2 Description of SWAT
SWAT is a continuous, physically based, semi-distributed hydrologic model first created by
the US Department of Agriculture (USDA) and the Texas Experimental Station (TES) in the
early 1990s. It was designed to calculate and route water, sediments and contaminants from
individual drainage units (sub-basins) throughout a river basin towards its outlet. It is a
versatile tool that has been used in many parts of the world to predict the impact of
management practices on water, sediment and agricultural chemical yields in large complex
basins with varying soils, land use and management conditions, over long periods of time
(Arnold and Fohrer 2005; Eckhardt et al. 2005).
The simulation of the watershed’s hydrological cycle is divided into two phases: the land
phase and the routing phase. For modeling the land phase, the river basin is divided into
subbasins, each one of which is composed of one or several hydrological response units
(HRUs), which are areas of relatively homogeneous land use/land cover and soil types. The
characteristics of the HRUs define the hydrological response of a sub-basin. For a given time
step, the contributions to the discharge at each sub-basin outlet point is controlled by the HRU
water balance calculations (land phase). The river network then connects the different subbasin outlets, and the routing phase determines movement of water through this network
towards internal control points, and finally towards the basin outlet (Neitsch et al. 2002, 2005).
2.3 Input Data
As a distributed watershed model, SWAT requires intensive geospatial input data to drive
watershed dynamics. The major geospatial input data include climate data, a terrain map, soil
properties and a land use/land cover map. The following datasets were prepared for the
SWAT Model with Fixed and Varied Parameterization Strategies
2827
Qinhuaihe Watershed study: (a) a series of land-use maps derived from multi-temporal and
multi-spectral satellite images in the basin over time; (b) daily rainfall data of the seven
raingage stations for the 21-year period; (c) daily discharge data of two gauging stations
covering the period January 1986 to December 2006; (d) daily maximum and minimum
temperature data during the period for the basin obtained from the China Meteorological
Data Sharing Service System; (e) a soil map at 1:75,000 scale in which the physical soil
layer properties (including texture, bulk density, available water capacity, saturated conductivity) were collected mainly from Jiangsu Soil Handbook and field observations; and (f)
Digital Elevation Model (DEM) of the Qinhuai River basin.
The digital land use maps were generated from a multi-temporal and multi-spectral
dataset. Thirty (30)-meter resolution Landsat Thematic Mapper (TM) images from 1988,
1994 and 2006, and Enhanced Thematic Mapper Plus (ETM+) images from 2001 to 2003
were used in this study. Supervised classification method with maximum likelihood clustering and DEM data were employed for image classification as a hybrid method to generate
land use maps. Land use categories included in land use maps 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. The
overall classification accuracy was over 89 % with kappa values over 0.79, meeting the
accuracy requirements. The land use maps for 5 years were shown in Fig. 2.
The land use changes from 1988 to 2006 were presented in Table 1. During 1988–2006,
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 25 %. Subsequently, the woodland
occupied over 17 %, and water area occupied a minor area. The urban development has been
recognized for over 19 years, and a high rate of urban expansion emerged after 2003 at the
Fig. 2 Land use maps of the Qinhuai River basin in five years
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J. Du et al.
Table 1 Land use structures of the Qinhuai River basin from 1988 to 2006 (in %)
Year
Impervious surface
Paddy field
Water
Woodland
Dry land
1988
3.4
47.8
4.0
18.5
26.3
1994
4.7
47.1
4.0
17.3
26.9
2001
6.6
45.0
4.2
17.5
26.7
2003
2006
7.6
12.1
43.9
41.6
4.1
4.0
17.9
17.2
26.5
25.1
expense of reducing the amount of other land use categories, especially the paddy field. From
1988 to 2003, the impervious surface increased from 3.4 % to 7.6 %; however, it increased to
12.1 % in 2006. On the other hand, the paddy field decreased from 47.8 % in 1988 to 41.6 %
in 2006, a record of continued loss of 6 %. Water area changed slightly, while woodland and
dry land decreased during the past 19 years. It should be noted that due to the policy of treeplanting, woodland represented an increasing trend during 1994 to 2003.
Soil data of the study area were generated from existing Soil Survey maps in
Nanjing and Jurong at scales of 1:75,000. Soil map were rectified and mosaiced, 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-aqvic soil (Fig. 3).
According to the hydrologic soil groups classification, developed by the U.S Natural
Resource Conservation Service (NRCS), only hydrologic soil groups B (paddy soil, purple
soil) and C (yellow-brown soil, limestone soil and gray fluvo-aqvic soil) are presented in the
basin (Fig. 4), indicating a moderate infiltration rate and a slow infiltration rate respectively,
when thoroughly wetted.
2.4 Model Setup
The basin and subbasin boundaries, as well as stream networks needed by SWAT were
delineated using terrain processing module of ArcHydro Tools software with ArcGIS
interface based on DEM data obtained from existing 1:50,000 scale contour map. The basin
was divided into 32 subbasins based on the threshold area of 2,500 ha.
The overlay of soil, land use maps and slope resulted in 488 HRUs, representing
homogeneous land use and soil. This discretization was trying to respect the original
distribution of soil and land use, while keeping the number of HRUs down to a reasonable
number. Meteorological data were introduced into the model, and databases of soil and land
use properties were edited and made available in this study area.
The Soil Conservation Service Curve Number (SCS CN) method was selected to
calculate surface runoff, the Hargreaves method was adopted to estimate Potential evapotranspiration (PET), and the Muskingum river routing method was used to route the water
through the channel network.
Three nested SWAT models for the Qinhuai River basin, with two outlets, were generated
by following steps: (a) separating the watershed into three parts (one upstream part and two
downstream parts, see Fig. 5) at the point of river diversion, and building SWAT model for
each part; (b) adding the outlet to model 1 of Part 1 (upstream part) and inlets to model 2 of
Part 2 and model 3 of Part 3 manually at diversion point with the help of “Watershed
SWAT Model with Fixed and Varied Parameterization Strategies
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Fig. 3 Soil map of the Qinhuai River basin
Delineation” module in SWAT; (c) building connections between inflow data files with
model 2 and model 3 in the “Edit Point Source Input” module. Inflow data for model 2 and
model 3 were obtained by divorcing the outflows generated from model 1 with the ratio of
observed flows in Part 2 and Part 3; and (d) running model 1 first and then model 2 and
model 3. In this way, the runoff at the outlet of upstream part could be distributed into two
downstream parts, resolving the defect of bifurcation in SWAT. Parameter adjustment
procedure will be discussed in the next section.
2.5 Automated Sensitivity Analysis
The SWAT model involves a large number of parameters which describe the different
hydrological conditions and characteristics across the basin. During the calibration process,
the first step was to determine which model parameters were the most influential in matching
the model simulated runoff to the observed runoff. This can eliminate or at least reduce some
of the limitations of manual calibration (Franczyk and Chang 2009). To help accomplish this
goal, the Automated Sensitivity Analysis tool provided by SWAT was used in the study
which employs the LH-OAT (Latin Hypercube Sampling-One at A Time) analysis method
(Van Griensven et al. 2006). Streamflow and historical meteorological data for the period
1986–1992 were used for sensitivity analysis. The results of SWAT Automatic Sensitivity
tool will provide general adjustment guidelines and reduce time-consuming in manual
calibration.
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Fig. 4 Hydrologic soil group map of the Qinhuai River basin
Fig. 5 Subbasins, stream networks and three Parts of the Qinhuai River basin
J. Du et al.
SWAT Model with Fixed and Varied Parameterization Strategies
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2.6 Calibration and Validation of SWAT with Fixed and Varied Parameterization Strategies
In this study, the model was calibrated and evaluated using a split sample procedure against
streamflow data collected at the outlets of the watershed. The adjustments of selected
parameters were performed with two strategies: fixed parameterization and varied
parameterization. Fixed parameterization strategy keeps the calibrated parameter values
constant during validation period (except CN2) while varied parameterization strategy
estimates parameter values using the relationship between parameters’ values and land
use pattern.
In order to statistically test the accuracy of the calibrated and validated runoff outputs,
two commonly used criteria, correlation coefficient (R) and model efficiency (E) proposed
by Nash and Sutcliffe (1970) were used.
SWAT was calibrated using a daily time step. The calibrated parameters will be selected
based on sensitivity analysis results and their relationship with land use condition. Spatially
varied values of soil physical properties (available water capacity, saturated conductivity,
bulk density, texture, and organic matter) assigned to different HRUs by SWAT were not
calibrated. The determination of model parameters was done manually, and the SWAT
Automated Calibration tool was not used in calibration because the basin was divided into
three parts, and one of them has no observed runoff data.
To calibrate and verify the SWAT model using fixed parameterization strategy, 21-year
(1986–2006) streamflow and historical meteorological data were used. The observed runoff
dataset was divided into a calibration period (1986–1995) and a verification period (1996–
2006). For model calibration, land use data for 1988 and meteorological data for 1986–1990
were used for 1986–1990 simulation, and land use data for 1994 and meteorological data for
1991–1995 were used for 1991–1995 simulation. Simulated runoff values at the outlets of the
second and third parts were added to get the final runoff values of the Qinhuai River basin.
In the fixed parameterization approach, validation was performed to define whether
model parameters derived during calibration were generally valid. SWAT was validated
using land use data for 2001 and meteorological data for 1996–2001 for 1996–2001
simulation, land use data for 2003 and meteorological data for 2002–2003 for 2002–2003
simulation, and land use data for 2006 and meteorological data for 2004–2006 for 2004–
2006 simulation.
To calibrate and verify the SWAT model using varied parameterization strategy, 4 years
land use data (1988, 1994, 2001 and 2003) and corresponding daily runoff and meteorological data were used for model calibration to obtain four parameter sets associated with
each land use scenarios. The relationships between the model parameters’ values and landuse changes were established from calibration results. The daily runoff data of year 2004 to
2006 and corresponding land use/cover map of year 2006 were then used for model
validation with parameters estimated based on the relationships between the model parameters and land-use scenarios.
3 Results and Discussion
3.1 The Sensitivity Analysis Results
A ranking of the “most sensitive” parameters, determined by means of a LH-OAT analysis is
given in Table 2. Parameter ranked as 1 is considered as most sensitive and ranked as 15 is
least sensitive and so on. Table 2 shows that the parameters representing the soil and
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J. Du et al.
Table 2 Results of sensitivity analysis
Rank
Parameters
Description
1
CN2
Curve number for moisture condition II
2
ESCO
Soil evaporation compensation factor
3
GWQMN
Threshold depth of water for return flow
4
5
CANMX
SOL_Z
Maximum canopy storage
Depth from soil surface to bottom of layer
6
ALPHA_BF
Baseflow alpha factor
7
BLAI
Potential leaf area index
8
GW_REVAP
Groundwater re-evaporation coefficient
9
CH_K2
Effective hydraulic conductivity in main channel
10
SOL_AWC
Soil available water capacity
11
GW_DELAY
Ground water delay
12
13
SURLAG
SOL_K
Surface runoff lag coefficient
Saturated hydraulic conductivity
14
EPCO
Plant uptake compensation Factor
15
CH_N(2)
Manning’s “n” value in main channel
vegetation properties, surface runoff, groundwater, and evaporation are more sensitive than
others, and accurate estimation of these parameters is important for streamflow simulation
with the SWAT model in the watershed.
3.2 The Calibration and Validation Results of SWAT with Fixed Parameterization Strategy
Not all of the parameters identified by sensitivity analysis were modified during calibration
in order to reduce the problem of overparameterization as far as possible. Following the
guidelines provided by Arnold et al. (2000) and Neitsch et al. (2002), five model parameters
were selected based on sensitivity analysis and shown in Table 3. Table 4 showed the finally
adjusted CN’s for land use and soil type compositions available in the basin.
Surface runoff is extremely sensitive to parameter CN2 (SCS runoff curve number for soil
moisture condition II), and decreasing the CN2 values results in decreased runoff and
increased infiltration, baseflow, and recharge. Evapotranspiration is sensitive to soil evaporation compensation coefficient (ESCO), increasing ESCO values results in decreased
evapotranspiration. Increasing threshold depth of water for return flow (GWQMN) results
in decreased baseflow; increasing surface runoff lag coefficient (SURLAG) and Manning’s
Table 3 Initial and finally adjusted parameter values of flow calibration
No.
Parameters
Range
Initial Value
−25 %–25 %
Default*
Adjusted value
1
CN2
2
GWQMN
0–5,000
0.00
Table 4
0.05
3
ESCO
0–1
0.95
0.83
4
SURLAG
0–100
1
4
5
CH_N(2)
0–150
0
0.03
*Variable default values in the subbasins according to the landuse and soil types
SWAT Model with Fixed and Varied Parameterization Strategies
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Table 4 Finally adjusted parameter values of CN
Hydrologic soil group B
Hydrologic soil group C
Paddy field
73
78
Wood land
60
73
Impervious surface
85
90
Water
Dry land
92
75
92
81
“n” value for the main channel (CH_N(2)) would result in lower velocities of water flow in
subbasin surface and the main channel. These parameters have clearer physical meanings,
i.e., CN2 was determined by soil type and land use type, and the values of GWQMN,
SURLAG and CH_N(2) will decrease when impervious surface areas of the basin increase.
The correlation coefficient, R and Nash-Sutcliffe efficiency E of each calibration and
validation period for daily runoff are shown in Table 5. The R and E of overall calibration
period are 0.87 and 0.86, respectively, and the R and E of overall validation period are 0.82
and 0.81 respectively. It can be seen from the table that the values of R and E change
considerably during validation periods, and smaller values are found during 2004–2006
period even though their values are high over the whole validation period. It can also be seen
that there is a considerable decrease in annual runoff estimation during 2004–2006 period.
Comparison of observed and simulated runoff hydrographs of calibration and validation
periods is shown in Figs. 6 and 7. The model validation results show that the calibrated
parameters could not simulate the stream flow very well during validation periods when
using the fixed parameterization approach.
3.3 The Calibration and Validation of SWAT with Varied Parameterization Strategy
3.3.1 Relationship between SWAT Parameters and Land Use Characteristics
Five parameters (CN2, ESCO, GWQMN, SURLAG and CH_N(2)), associated with land
use (predominant urbanization) were selected to adjust for the periods of 1986–1990, 1991–
1995, 1996–2001 and 2002–2003 with corresponding land use patterns of 1988, 1994, 2001
and 2003 separately. CN2 values for each period were determined by land use and soil types
for the corresponding land use year, and other parameter values for each selected period
reflecting land use pattern were obtained by running SWAT with trial and error method based
Table 5 Calibration and validation statistics of daily flow simulation
Calibration
Periods
1986–1990
Validation
1991–1995
1996–2001
2002–2003
R
0.83
0.88
0.77
0.92
E
0.82
0.88
0.74
0.92
2004–2006
0.72
0.67
Simulated total runoff (mm)
1,662
2,067
2,136
1,216
1,024
Observed total runoff (mm)
1,578
1,919
1,897
1,297
1,222
Total runoff error (%)
5.32
7.71
12.6
−6.25
−16.20
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Fig. 6 Observed and simulated runoff hydrographs of the Qinhuai River basin in 1991–1995
on the resulting water balance and hydrograph. The results of the calibrated parameters,
evaluation criteria and impervious surfaces of each period are listed in Table 6. The table
shows that SWAT performs well in the simulations for four periods with varied values of
calibrated parameters in terms of R, E and water balance error.
Based on the calibration results, the regression equations relating model parameters and
the impervious area as shown in Table 3 are established for 3 parameters as follows:
GWQMN ¼ 1:068 IM 3:072
R2 ¼ 0:965
ð1Þ
SURLAG ¼ 0:281 IM þ 5:243
R2 ¼ 0:955
ð2Þ
CH N ð2Þ ¼ 0:001 IM þ 0:034
R2 ¼ 0:986
ð3Þ
where IM is the percentage of the impervious area over the watershed.
Although the parameter ESCO showed a linear relationship with impervious area when
impervious area is less than 7.6 %, there was a sharp increase in ESCO when impervious
reached 7.6 % and ESCO quickly approached its physical upper value of 1. The relationship
Fig. 7 Observed and simulated runoff hydrographs of the Qinhuai River basin in 2004–2006
SWAT Model with Fixed and Varied Parameterization Strategies
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Table 6 Calibration results of flow simulation for selected years with varied parameterization approach
Periods
1986–1990
1991–1995
1996–2001
2002–2003
Impervious area(%)
3.4
4.7
6.6
7.6
ESCO
0.77
0.80
0.81
0.92
GWQMN
0.9
1.4
4.2
5.0
SURLAG
CH_N(2)
4.2
0.030
4
0.029
3.5
0.026
3
0.025
R
0.83
0.88
0.78
0.93
E
0.82
0.88
0.76
0.93
Simulated total runoff (mm)
1,569
1,906
1,955
1,291
Observed total runoff (mm)
1,578
1,919
1,897
1,297
Total runoff error (%)
−0.57
−0.68
−0.46
3.06
between ESCO and impervious area cannot be established when the impervious area is
larger than 7.6 %.
3.3.2 Verification of the Regression Equations
To test and verify the usability of the regression equations in calculating the parameters
GWQMN, SURLAG, CH_N(2) in the basin, the period 2004–2006 was chosen as the
verification period. The values of GWQMN, SURLAG, CH_N(2) were determined by the
established regression equations according to the impervious area in 2006. CN2 was
calculated according to land use and soil types in 2006. The value of ESCO was determined
to be 0.98 which showed best result when 0.94, 0.96, and 0.98 were used for model
simulation for the period 2004–2006. Table 7 presents the simulation results with varied
and fixed parameterization approaches. In Table 7, a considerable improvement in annual
runoff estimation can be seen from using varied parameterization as compared with that
using fixed parameterization, and remarkable improvements in the R and E values could also
be found.
To demonstrate more clearly the improvement of model simulated flood events in using
varied parameterization approach over the conventional fixed parameterization approach,
Table 7 Results with varied and fixed parameterization approaches for 2004–2006 simulation
Varied parameterization
Impervious area(%)
12.1
Fixed parameterization
12.1
ESCO
0.98
SURLAG
1.84
0.83
CH_N(2)
GWQMN
0.022
10.1
0.03
0.05
R
0.77
0.72
E
0.76
4
0.67
Simulated total runoff (mm)
1,178
1,024
Observed total runoff (mm)
1,222
1,222
Total runoff error (%)
−3.60
−16.20
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J. Du et al.
two flood events observed during the year 2006 were selected to compare their performance.
Figure 8 shows the observed and simulated hydrographs of the two flood events using fixed
and varied parameterization approaches.
It can be seen from Fig. 8 that the simulated runoff based on varied parameterization
resembles the observed runoff more closely than that based on fixed parameters. The R
increased from 0.82 to 0.93 and E from 0.75 to 0.88 for the first flood event, and R changed
from 0.56 to 0.73 and E from 0.48 to 0.71 for the second flood event indicating large
improvements in both R and E. The results show that it is necessary to simulate the
hydrological processes with varied parameterization approach under changing land use
condition.
4 Summary and Conclusion
In the literature, SWAT was widely used to simulate the hydrological impact of land
use change by altering the value of CN, the major parameter determining surface
runoff, according to the land use change scenarios. Two types of challenges are
usually faced in such studies. First, like many other distributed hydrological models,
SWAT also suffers from the problem of overparameterization and equifinality [i.e.,
there exists more than one combination of parameter values that may result in the
same model output (Beven 1993)]. Second, the hydrological impact of land use
change is usually simulated by altering the CN values, while keeping other parameters
constant. However, land use change can alter not only direct runoff, but also other
fluxes such as evapotranspiration, velocities of overland flow and channel flow and so
on. In this study, attempts were made to address these two challenges. In order to
avoid overparameterization as far as possible, parameter sensitivity analysis was first
made, and based on which only five sensitive parameters are calibrated and other
parameters are determined by using physical data of the catchment. To address the
second challenge, those parameters associated with evapotranspiration and velocity of
runoff routing and so on were also allowed to change with new land use scenarios,
which was achieved by developing a varied parameterization approach. In this approach, regression equations are established with selected parameters (GWQMN,
SURLAG, CH_N(2)) as dependent variables and change of impervious area as
independent variable, so that the hydrological impacts of land use change can better
be simulated with varying parameters according to land use change scenarios. The
performance of the model with varied parameterization approach was compared with
Fig. 8 Observed and simulated runoff hydrographs of two flood events with fixed and varied parameterization approaches of the Qinhuai River basin
SWAT Model with Fixed and Varied Parameterization Strategies
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the conventional fixed parameterization approach in simulating the hydrological impact of urbanization. The following conclusions are drawn from the study.
(1)
(2)
The parameters that are more sensitive and best describing relations between land use
and hydrological processes should be selected for calibration under changing land use
condition to better address the problem of overparameterization and the problem of the
equifinality in distributed models.
Model performance with varied parameterization approach shows a large improvement
compared with that with conventional fixed parameterization approach. This study
shows that when assessing hydrologic impact of land use change, establishing a
relationship between parameter values and easily observable catchment characteristics
could render the parameterization of land use scenarios more realistically, leading to a
more accurate impact prediction.
It should be noted that the varied parameterization approach was implemented by relating
three parameters solely with the percentage of the impervious areas in the catchment,
without considering the changes in other land use types. When the changes in other land
use types are more important, other types of regression equations need to be established.
Further research needs to be directed along these lines in other regions.
Acknowledgements 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.
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