Development and test of SWAT for modelling hydrological process in irrigation disticts with paddy rice

Journal of Hydrology 396 (2011) 61–71

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Journal of Hydrology
journal homepage: www.elsevier.com/locate/jhydrol

Development and test of SWAT for modeling hydrological processes
in irrigation districts with paddy rice
Xianhong Xie a,b,⇑, Yuanlai Cui a
a
b

State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

a r t i c l e

i n f o

Article history:
Received 9 December 2008
Received in revised form 7 May 2010
Accepted 22 October 2010
This manuscript was handled by K.
Georgakakos, Editor-in-Chief, with the
assistance of Michael Brian Butts, Associate
Editor
Keywords:
Model development
SWAT

Hydrological process
Irrigation district
Paddy rice

s u m m a r y
The water movement in irrigation districts, especially for paddy rice cultivation, is characterized by complicated factors. Soil and Water Assessment Tool (SWAT) is a popular tool for understanding the hydroagronomic processes. However, it fails to simulate the hydrological processes and crop yields in paddy
rice areas. In this study, we develop the SWAT model by incorporating new processes for irrigation
and drainage. The evapotranspiration process in paddy fields is simulated on the basis of water storage
conditions, and a controlling irrigation scheme is introduced to manage the irrigation and drainage operations. The irrigation function of local water storages, such as ponds and reservoirs, is extended for these
storages in order to provide water in a timely manner to paddy fields. Moreover, an agronomic model is
incorporated to estimate crop yields when available data sets are not satisfactory. The model is tested in
Zhanghe Irrigation District, China. The simulated runoff matches well to the measurements and the
results indicate the developed model is preferable to the original edition of SWAT. The estimate of the
paddy rice yield is acceptable and the dynamics of water balance components approximately characterize
the state of water movements in paddy fields. Therefore, the developed framework for SWAT is practical
and capable of representing the hydrological processes in this irrigation district. Further work is still
needed to more broadly test the model in areas with paddy rice cultivation.
Ó 2010 Elsevier B.V. All rights reserved.

1. Introduction
Paddy rice, as a major food crop in China, consumes large
amounts of water for agricultural irrigation. It is important to create a reasonable framework to evaluate productivity and manage
water resources in irrigation districts where the hydrological cycle
depends not only on natural factors (e.g. the evapotranspiration
and precipitation), but also on human activities (e.g. the irrigation
and drainage operations). Especially in the paddy rice areas, the
different water bodies (e.g. the ponds, reservoirs and paddy fields)
and constructions (e.g. the irrigation canals) are highly distributed.
Thus, the irrigation district is a human-nature composite ecosystem (Wang and Yang, 2005), and a coupled hydro-agronomic model is needed to explore the hydrological processes and crop growth
conditions in this kind of area (Luo et al., 2008).

There are a number of sophisticated models able to address
these challenges, such as Soil–Water–Atmosphere–Plant (SWAP,
Van Dam et al., 1997; Kroes et al., 1999), MIKE SHE (Graham and
Butts, 2006), and Soil and Water Assessment Tool (SWAT, Arnold
⇑ Corresponding author. Present address: Room 301, Founder Building, No. 298,
Chengfu Road, Haidian District, Beijing, China. Tel.: +86 10 58809071; fax: +86 10
82887918.
E-mail addresses: (X. Xie), (Y. Cui).
0022-1694/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.jhydrol.2010.10.032

et al., 1993). The SWAP model simulates vertical water flow, solute
transport, heat flow in close interaction with crop growth in agricultural fields. This permits water productivity analysis and estimation of the agricultural water use (Singh et al., 2006; Anuraga
et al., 2006; Utset et al., 2006, 2007; Mandare et al., 2008). However, this model focuses on hydrological processes at the field
scale, and it is not suitable for large scale simulations or the areas
with great spatial variability. Furthermore, the ponding boundary
at the ground surface is not considered in the model. So it is better
at modeling upland areas rather than depression areas (e.g. the
paddy fields). As a fully physically-based hydrological model, MIKE
SHE accounts for many hydrological processes and their interactions as well as water management practice (DHI, 2007). It is also
widely used to simulate the hydrological water balance and
investigate the effects of cropping practices in irrigation districts
(Jayatilaka et al., 1998; Singh et al., 1999; Islam et al., 2006), and
evaluate sustainable groundwater management options (Demetriou and Punthakey, 1999). However, its performance depends
highly on detailed information and abundant data sets from the
area of interest, and scaling issues of parameters and variables
are great challenges (Xiong and Guo, 2004).
While SWAT is a basin scale, physically-based continuous distributed model developed to predict impacts of management on

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X. Xie, Y. Cui / Journal of Hydrology 396 (2011) 61–71

water, sediment, and agricultural chemical yields in ungauged
watersheds (Neitsch et al., 2001). It allows for relatively complete
agricultural management practices (e.g. planting, fertilization, irrigation and drainage) and spatial distributed characteristics (e.g.
ponds, reservoirs) in irrigation areas. So SWAT is considered as a
preferred tool for the agricultural watershed modeling in this
study.
Recently, there have been a few studies concerning hydrological
processes based on SWAT in irrigation areas. Ritschard et al. (1999)
used SWAT to estimate the irrigation water requirements and
monthly runoff in the Gulf Coast of the United States. Their results
showed the capability of SWAT to deal with large scale problems.
Bosch et al. (2004) evaluated the SWAT model on a coastal plain
agricultural watershed, and suggested that a modification and
more extensive calibration may be necessary to increase the accuracy of the daily flow estimation. Behera and Panda (2006) identified the critical areas of an agricultural watershed and
recommended the best management practices using SWAT, and
their works revealed the robust performance of the model in different simulation conditions. Since data scarcity is a common problem in hydrological modeling, Immerzeel and Droogers (2008)
integrated remote sensing and observed monthly discharge to calibrate the SWAT model. In addition, Luo et al. (2008) assessed the
crop growth and soil water modules in SWAT2000 based on field
experiments in an irrigation district of the Yellow River Basin (in
China), and they proposed some improvements to soil water and
groundwater evaporation modules. There are other studies concerning the application of SWAT. Comprehensive reviews on SWAT
model were given by Gassman et al. (2007), and Krysanova and
Arnold (2008).
When the current SWAT model is used in irrigation districts
with depressions or impounded areas, e.g. the paddy fields, however, it may cause some bias because it is not able to completely
represent the characteristics of fields and address complicated

water management practice.
In this study, we focus on the simulation of hydrological processes in paddy rice areas and propose developments to the current SWAT framework. We first give an overview of SWAT
mainly on the hydrological cycle in paddy fields and comment on
its weaknesses. In Section 3, the developed components are illustrated, including the evapotranspiration, the processes of irrigation
and drainage, and the crop yield estimation. In Section 4, the developed model is examined in Zhanghe Irrigation District (ZID), in
China. The runoff and crop yields are calibrated and validated and
the water balance in paddy fields is evaluated. In Section 5, a discussion is presented and finally conclusions are given for this study.

2. Model description
2.1. Structure of land phase processes
SWAT simulates major hydrological components and their
interactions as simply and yet realistically as possible (Arnold
et al., 1993). To realize this capability, a sequential structure combined with thirteen routing commands is used to simulate hydrological processes occurring within hydrologic response units
(HRUs) and subbasins and to route stream loadings through the
channel network in a watershed.
The first and important loop run is a subbasin command in
which the land phase process of the hydrological cycle is simulated, including surface and subsurface runoff generation, snow fall
and melt, vadose zone processes (i.e. infiltration, evaporation, lateral flows), crop growth and water quality transformation. Paddy
fields in a subbasin are aggregated and treated as a pothole, like
an impounded or depression area.

Fig. 1 shows the flowchart on the land phase process. We should
emphasize that the simulation scheme of the model distinguishes
between different kinds of land cover. (1) If an HRU is covered with
water, only evaporation from the water body is simulated with the
Priestly–Taylor equation and no water from this HRU will contribute to stream flows. In fact, the water movement in this kind of
HRU can be better represented as processes in ponds, wetlands
or reservoirs (Neitsch et al., 2001). (2) If an HRU in a released state
is covered with general lands (pot_vol < e), then the surface runoff
is estimated by the curve number technique or the Green–Ampt

method, and the actual evaporation from soil water is computed.
Here, paddy fields are treated as upland areas. (3) If the HRU contains impounded potholes in which water is stored, then the surface runoff and the actual evaporation from soil profile are
excluded, while the water routings in potholes, such as inflow,
evaporation from water body and seepage, are taken into account
in the pothole procedure.
Therefore, SWAT is a comprehensive and reasonable model that
is suitable for most of conditions in irrigation districts.
2.2. Main components
Three main components with respect to growth environment of
the paddy rice are further described here.
2.2.1. Water routing in potholes
A pothole, originally meaning a deep and round hole or a pit, is a
depression that can receive a part of surface runoff from the related
HRUs. SWAT assumes the paddy rice could grow in this area.
Accordingly, the paddy field in this kind of HRU is assumed to be
a cone shape (Fig. 2), and its surface area of water body is varied
with the depth or the volume of water storages (Neitsch et al.,
2001).
The water balance components in potholes contain precipitation, irrigation, surface runoff concentration, evaporation from
water body, seepage and outflow. Since the pothole is characterized with a cone shape, the volume of precipitation depends on
the surface area of water body as well as precipitation intensity.
Irrigation water applied to a pothole is obtained from one of the
five types of water source: a reach, a reservoir, a shallow aquifer,
a deep aquifer or an outside source. Water can be removed from
potholes to stream reaches through three different routes; overflows, release operations and tile drainages.
However, these representations are appropriate for general
closed depression areas rather than real-world paddy fields (Fig. 3).
First, when an HRU containing potholes is impounded, the surface
runoff from the non-pothole part is not considered (Fig. 1). Even
though the other pothole processes concerning paddy fields are represented, this framework still results in underestimation of the surface runoff loading to main channels. Second, the surface area of the

water body is a fluctuating value which varies with the volume of
water stored in the impounded pothole. In contrast, the actual paddy
fields are characterized by a large number of plots or fields and separated by low embankments that retain ponding water on the soil
surface (Kang et al., 2006). Thus the areas of paddy fields remain
approximately constant in the whole process. This inconsistency
can also underestimate the surface area of paddy fields which influences the subsequent hydrological processes. Furthermore, in a large
irrigation area it is difficult to specify a reasonable value for the fraction of HRU area that drains into the related pothole.
2.2.2. Water routing in ponds
Ponds are water bodies located within a subbasin that received
inflow from a fraction of the subbasin area. It is assumed in SWAT
that ponds are uniformly distributed in each HRU in a subbasin. In
addition to general components of water balance in ponds, the


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X. Xie, Y. Cui / Journal of Hydrology 396 (2011) 61–71

Beginning for
an HRU
Initialization for
variables

Evaporation
calculation

Yes

Is the pothole
contained ?

No

Yes
Is water body ?
No

Surface runoff
generation

Hydrological processes in
potholes with the cultivation
of paddy rice

Groundwater routing

Yes

Pot_vol < ε
and released ?
No

No

Is water
impounded ?
Yes

No

Is the pond
Contained ?

Update volume of
water in pothole

Yes
No

Water balance
calculation in ponds

Pot_vol > 0?
Yes

Soil water routing

No

Is the Irrigation
specified?
ET0 calculation
Yes

Pothole processes ,
including inflow ,
outflow, drainage ,
infiltration ,
evaporation and
release /impounding

operations , etc.

Yes
Irrigation operation s

Pot_vol > ε?
No
Actual ET simulation
( Es and Ecan are
calculated )

Consumptive water
uses

Output treatment
Crop growth routing
( Ep is calculated )

Next HRU
simulation

Note: Pot_vol is the volume of water
stored in a pothole ; ε is a infinitesimal ;
ET0, Ecan, Ep, and Es are potential
evapotranspiraiton ; evaporation from
free water in the canopy , plant
transpiration , and evaporation from soil
profile, respectively .

Fig. 1. Flowchart on the land phase simulation (only modules regarding agricultural hydrology are expressed).

Area of an HRU

Cone shape of the Pothole

Contributing area
for a pothole
Pothole

SA
H

slp

V
1

Note: SA is the surface area of the water
3
body, ha; V is its volume , m ; H is the
depth , m; and the slp is the average slop
of a specified HRU .
Fig. 2. Schematic diagram of the area of an HRU (left) and its related pothole with the cone shape (right).

consumptive water use item is also considered to estimate the irrigation for crops outside the watershed or removal of water for urban/industrial use. However, the irrigation function from ponds for
local crop fields is not taken into account, which will limit model
applications to water management scenarios, such as the real-time
irrigation and drainage based on the local source of water (Guo,
1997).
2.2.3. Crop growth

SWAT incorporates a simplified version of the Erosion-Productivity Impact Calculator (EPIC) plant growth model. In this model,
the phenological plant development is based on daily accumulated
heat units, potential biomass is based on a method developed by
Monteith (Monteith, 1977), a harvest index is used to calculate
yield, and plant growth can be inhibited by temperature, water,
nitrogen and phosphorus stress (Neitsch et al., 2001). When

applied to large areas, it still suffers from scarce data availability,
such as fertilization and pesticide information.
3. Model development
3.1. Evapotranspiration process in paddy fields
As the paddy field is a plot separated by low embankments that
occupy only a very small proportion of the total area of the field, it
is reasonably to assume that the area of paddy fields is equal to the
area of the HRU:

SA ¼ AHRU

ð1Þ

where SA is the field surface area (ha); AHRU is the area of the HRU
whose land cover is the paddy rice (ha). Note that here the paddy
field has a cuboid shape with a constant surface area rather than


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X. Xie, Y. Cui / Journal of Hydrology 396 (2011) 61–71

3.2. Framework of irrigation and drainage controlling

Ecan and Ep
Paddy rice
Rainfall

Irrigation
canal

Irrigation

Epot or Es
Drainage
Hp

Drainage
canal

hmax
hmin
Ql

A good controlling scheme for irrigation and drainage at the field
scale should not only provide right moisture conditions to favor crop
growth, but also save water and minimize water transfer. Moreover,
the local source of water in ponds or pools distributed in irrigation
areas, could be conveniently used for crop irrigation to reduce water
transfers from other sources that may be outside the agricultural
watershed. Therefore, controlling schemes for irrigation and drainage and the utilization of local water are widely adopted practices
for agricultural water management to save water transfers. They
also change the route of runoff in an agricultural watershed.

Root layer
Seepage
Fig. 3. Schematic diagram of a paddy field. (hmin, hmax and Hp denote the three
critical depths; Ecan, Epot and Es denote the three kinds of evaporation from the free
water in canopies, the water body surface and the soil water respectively; Ep
denotes the crop transpiration.

a round hole with a cone shape, which implies that the paddy rice
grows over the entire area of an HRU. So the precipitation, evaporation and transpiration can act on the entire land surface.
Moreover, in China, a controlling irrigation scheme is a generally implemented practice in paddy areas in order to save irrigation water and ensure considerable crop yields. The water depth
in paddy fields is variable and even approaches zero sometimes
when the paddy field is in dry state. Accordingly, two water
storage conditions are defined to calculate the actual evapotranspiration.
(1) If the paddy field is in dry state (pot_vol < e) and the HRU is
not an impounded area or a drained area, then

ET act ¼ Ecan þ Ep þ Es

ð2aÞ

(1) If the paddy field is in a wet state and the HRU is impounded,
then

ET act ¼ Ecan þ Ep þ Epot

ð2bÞ

where ETact is the actual amount of evapotranspiration occurring in
an HRU on a given day (mm, H2O); Ecan is the amount of evaporation

from free water in the canopy on a given day (mm, H2O); Ep is the
amount of plant transpiration on a given day (mm, H2O); and Es, Epot
are the water evaporation from the soil profile and the water body
surface respectively (mm, H2O).
For the first condition, the equation defines a general state that
the land surface is exposed with no water stored in potholes or
fields, thus the evaporated water is from the soil water. This kind
of HRU could be fallow fields, or paddy fields in dry-state periods.
For example, in the final tillering stage, the fields should be kept at
a dry state via drainage operations in order to control useless tillers
of the paddy rice and improve the aerating and temperature conditions (see Table 1). This operation is so-called sun drying of the
paddy field (Li et al., 2003). For the second condition, the fields
are impounded and water is stored, so the evaporation (Epot) is
from the water body instead from the soil profile.

3.2.1. Irrigation and drainage for paddy fields
In order to create favorable conditions with appropriate moisture, ventilation and temperature during the growth period, it is
usual to design a scheme to regulate water depths through irrigation and drainage at different growth stages of the paddy rice. Guo
(1997) introduced a technique with three critical depths, namely
the minimum fitting depth (hmin), the maximum fitting depth
(hmax) and the maximum ponding depth (Hp). As shown in the
Fig. 3, with water being depleted in fields (e.g. evapotranspiration
and seepage), the depth could reach a minimum fitting value, hmin,
then the moisture conditions may threaten the paddy rice. Subsequently the irrigation operation is requested and implemented until the depth reaches a maximum fitting value, hmax. On the other
hand, if significant precipitation occurs during the stage, the water
depth should be controlled under a maximum value Hp via the
drainage operation. This technique is widely used as it is simple
but effective for farmers. Therefore, it is important to design a reasonable scheme for the three controlling depths (hmin $ hmax $ Hp)
in different growing stages for the paddy rice. This is beyond the
scope of this paper, and the reader is referred to Guo (1997),

Anbumozhi et al. (1998) and Chi et al. (2001).
As mentioned before, in the SWAT model, the daily water balance equation can be updated as follows:

ST i ¼ ST iÀ1 þ P i þ IRi À DRi À ET i À SPi

ð3Þ

where ST is the water depth in fields (mm, H2O); P is the daily precipitation (mm, H2O); IR is the irrigation depth (mm, H2O); DR is the
drainage depth (mm, H2O); ET is the evapotranspiration (mm, H2O);
and SP is the seepage (mm, H2O). The subscript i denotes day i. The
evapotranspiration of paddy fields is computed with Eq. (2). While
the water lost by seepage through the bottom of paddy fields on a
given day is calculated as a function of the water content of the soil
profile beneath the pothole (Neitsch et al., 2001).
The irrigation depth or volume is represented as:

IRi ¼ hi;max À ST i

if ST i < hi;min

ð4aÞ

IRi ¼ 0 if ST i P hi;min

ð4bÞ

where hi,max and hi,min are the maximum and minimum fitting
depth respectively (mm, H2O). Similarly, the drainage depth (DR)
is written as

Table 1
Three critical depths of paddy fields in different growing stages in ZID with the intermittent irrigation technique.
Item

Date
Length/(day)
Depth/(mm)

Steeping stage

5/12–5/24
13
20–40–80

Recovering stage

5/25–6/2
9
10–30–50

Tillering stage

Booting stage

Wet

Dry

6/3–7/2
30

10–40–60

7/3–7/9
7
0–0–0

7/10–7/25
16
20–50–70

Heading stage

7/26–8/4
10
20–50–70

Milky stage

8/5–8/13
9
10–40–60

Ripening stage
Wet

Dry

8/14–8/21
8
0–20–30

8/22–8/29
8
0–0–0


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X. Xie, Y. Cui / Journal of Hydrology 396 (2011) 61–71

DRi ¼ ST i À Hi;p

if ST i > Hi;p

ð5aÞ

DRi ¼ 0 if ST i 6 Hi;p

ð5bÞ

where Hi,p is the maximum water depth at the day i (mm, H2O).
3.2.2. Irrigation water from ponds
Ponds are small reservoirs located in irrigation areas and they
allow farmers to capture rainfall and store surplus water from
other sources (Shahbaz et al., 2007a) that can then provide irrigation water to crops when required. In ZID, for example, thousands
of medium- and small-size ponds or reservoirs contribute onefourth of the amount of water in paddy fields, and they effectively
reduce the need for water transfers from the main Zhanghe reservoir (Shahbaz et al., 2007b).
For these reasons, the real-time irrigation function of ponds
should be extended in hydrological models. The water balance
equation is now expressed as:

V i ¼ V iÀ1 þ V i;pcp þ V i;flowin À V i;flowout À V i;ev ap À V i;seep À V i;use À V i;irr
ð6Þ
where Vi and ViÀ1 is the volume of water stored in ponds at the end
of the day i and i À 1 (m3 H2O); Vi,pcp is the volume of precipitation
falling on the water body during the day (m3 H2O); Vi,flowin and
Vi,flowout are the volume of water entering and leaving the water
body during the day(m3 H2O); Vi,evap is the evaporation volume of
water body during the day (m3 H2O); Vi,seep is the volume of water
lost from the water body by seepage during the day (m3 H2O); Vi,use
is the volume of water used for the urban or industrial requirement
during the day (m3 H2O); and Vi,irr is the volume of irrigation water
provided for local fields during the day (m3 H2O).
The calculation of each item in Eq. (6) refers to Neitsch et al.
(2001). With regard to the volume of irrigation water, there should
be water requirements from fields on the one hand and an enough
capacity to provide water from ponds on the other hand. This is
specified as:

V i;irr ¼ 10 Á ðhi;max À ST i Þ Á SA;

if ST i < hi;min

and V i

> 10 Á ðhi;max À ST i Þ Á SA
V i;irr ¼ V i ;
V i;irr ¼ 0;

if ST i < hi;min

ð7aÞ
and V i 6 ðhi;max À ST i Þ Á SA

else

ð7bÞ
ð7cÞ

where Vi,irr is the volume of irrigation water to local fields during
the day i (m3 H2O); Vi is the volume of water stored in ponds at
the end of the day i (m3 H2O); hi,max and hi,min are the maximum
and minimum fitting depth for crops at the day i respectively
(mm, H2O); STi is the ponding water depth in fields at the end of
the day i (mm, H2O); and SA is the area of paddy fields (ha). Clearly,
Eq. (7) is consistent with Eq. (4) on the irrigation volumes.
3.3. Simplified modeling of crop yields
The lack of available data at large scale is a common problem for
the crop growth simulation and it will limit the application of EPIC
model. So it is more practical to search for a simplified method to
estimate the crop yields. From previous research, crop growth and
yield generation are greatly influenced by the total volume of
evapotranspiration over the whole growth period. Li et al. (2003)
found that the relative grain yield is dependent on the relative
evapotranspiration volume with a linear or non-linear relation. A
lot of existing studies support such relations (Henry et al., 2007).
In this work, we utilize the linear function proposed by Stewart
et al. (1975), which can be described as

1À



Ya
ET a
¼ Ky 1 À
Ym
ET m


ð8Þ

where Ya is the actual crop yield (kg/ha); Ym is the maximum expected crop yield for a standard condition (no shortage of soil water
for crop growth, kg/ha); ETa is the actual crop evapotranspiration
(mm, H2O); ETm is the crop evapotranspiration for standard conditions (mm, H2O); Ky is a yield response factor that describes the
reduction in relative yield according to the reduction in ETm caused
by soil water shortage (Allen et al., 1998).
From Eq. (8), we can see that the Stewart model does not consider influences from moisture stress and fertilization conditions
at different growth stages, but it is capable of predicting crop yields
with only three parameters. Even though other models may provide accurate predictions allowing for more factors, they need
many more parameters to be identified beforehand. Therefore,
the Stewart model is popular in agricultural water resources programming and economic analysis and it has been recommended
by FAO (Allen et al., 1998; Li et al., 2003; Tolk and Howell, 2008).
4. Model application
4.1. Demonstrational area and data
4.1.1. Description of the irrigation area
In this section, the developed SWAT model is applied in a
subbasin of Zhanghe Irrigation District located in Hubei Province,
China (Fig. 4). The irrigation water for the area is mainly from
Zhanghe reservoir through the two main canals. In addition, there

are thousands of medium-sized and small ponds providing water
for irrigation and a complicated but effective irrigation canal system has been designed to transfer water from ponds and reservoirs
to the fields.
The selected area covers 112 891 ha of which the paddy rice accounts for 41%, followed by upland crops (18%), forest (16%), bare
land (10%), water (9%) and urban (6%). So paddy rice is the main
crop in this area (Fig. 5). The soil textures are mainly clay (82%)
and loam (18%) soil. Moreover, the study area is sloping, with elevations ranging from 450 m above sea level in the northwest to
20 m in the southeast. About 80% of the irrigation area lies in the
hilly region. This area has a typical subtropical climate with an annual mean temperature of 17 °C. In most years, there are between
246 and 270 frost-free days. Average annual rainfall is 970 mm,
although rainfall varies substantially from year to year depending
upon the monsoon (Shahbaz et al., 2007a). Thus this area as one
of the large irrigation districts in China is very suitable for paddy
rice.
4.1.2. Data set
An application of SWAT to a basin needs general data, including
topography, soil, land use, climate data and stream flow series. A
90 m resolution Digital Elevation Model (DEM) was obtained from
Chinese Academy of Sciences (CAS). The land use map with a resolution of 14.25 m was derived from remote sensing data (Landsat
ETM+) in the years of 2000 and 2001 and an unsupervised method
was used to classify the land use types (Cai, 2007). Since the land
use pattern in this area has not been changed significantly since
2000, it was reasonable to implement our calibration and validation based on data sets of 2005 and 2006. The digital soil map
accompanied by a database with soil properties was obtained from
the local agriculture department of ZID. Moreover, daily data sets
for the radiation, wind speed, relative humidity, and air temperature from January 1972 to December 2006 were obtained from
Tuanlin experimental station (Fig. 4) and they were mainly used
to calculate reference evapotranspiration. The daily precipitation
data sets (from January 1972 to December 2006) were available

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X. Xie, Y. Cui / Journal of Hydrology 396 (2011) 61–71

Zhanghe
Reservoir

China

Zhanghe Irrigation
District

Tuanlin

Zhouji

Xin
Fu
Zhangchang

Hubei Province

River

Shili
Zhanghe
Reservoir

Jingmen

(City)

Changhu
Lake

Sifang

Wuhan
(City)

Outlet

Fig. 4. Location of the demonstrational area of ZID.

Fig. 5. Elevation distribution (left) and land use map (right).

from five stations and daily runoff series of the outlet were obtained in the paddy growth period in the years of 2005 to 2006.
In addition, the irrigation records including the time and the quantity of irrigation were collected from the Management Bureau of
Zhanghe Reservoir.
In Fig. 5, the land cover marked with water are ponds or reservoirs are simulated as pond processes because there are no distinct
differences for the two kinds of water objects in our study area and
both of them provide the paddy rice area with irrigation water. The
characteristic parameters of ponds, such as the surface area and
the hydraulic conductivity, were specified from investigations of
typical ponds in this area. Furthermore, crop yields, as the weight
of the paddy rice grain per field area, were collected from 116 typical fields, as shown in Fig. 8. In each field, we only picked six
square meters to measure the grain weight.
Lastly but not less importantly, the three critical depths for paddy fields should be specified beforehand according to the irrigation
scheme. In ZID, the intermittent irrigation technique (IIT) as a favored scheme is widely implemented to regulate the water depth

in paddy fields. For more information about this irrigation scheme,
one can refer to Mishraa et al. (1990), Mao (1997), Anbumozhi
et al. (1998), and Wang et al. (2005). Here we just present the
parameters shown in Table 1. The growing period of the paddy rice
is about 110 days and it can be divided into seven growth stages
identified with different water controlling depths. The seedling is
transplanted on May 25, and the ripe paddy rice is harvested on
August 29. It should be noted that there are two dry-state periods
with no evaporation from water body, one is in the final tillering
stage and the other is in the final ripening stage.
4.2. Model evaluation criteria
Here we apply three commonly used indicators to evaluate the
efficiency and performance of the developed model. The first one is
the Pearson coefficient (R2) which is a good indicator to evaluate
the correlation of observed and simulated results. A value of 1 represents perfect correlation, while a value of 0 indicates they are
uncorrelated. The second one is the relative error coefficient (RE)


67

X. Xie, Y. Cui / Journal of Hydrology 396 (2011) 61–71

that represents the difference between observations and simulations. It is expressed as:

Pn
RE ¼

i¼1 ðM i À
Pn
i¼1 Oi

Oi Þ

Á 100%

ð9Þ

where n equals the total number of observations, Oi and Mi are the
observed and simulated values, respectively, on time step i.
The third indicator is the Nash–Sutcliffe model efficiency (Ens)
which is given by:

Pn
ðMi À Oi Þ2
Ens ¼ 1 À Pni¼1
2
i¼1 ðOi À hOi iÞ

ð10Þ

where hOii denotes the mean value of the long-term observations,
and the others terms are defined above. The values of Ens range between À1 and 1 and the higher the value the more efficient the calibration. A negative value indicates that the mean value of the
observations would have been a better predictor than the simulated
values (Immerzeel et al., 2008).
4.3. Model calibration and validation
The runoff and the crop yield data sets were used to test our
developed model by comparing the simulated and observed values.
The crop yield only includes the paddy rice since it is the main crop
in this area (accounts for 70%) and the information for other crops
is difficult to collect.

In the calibration process, a number of parameters in SWAT
model need to be adjusted either manually by users or by a computerized optimization algorithm, until a ‘best fit’ parameter set
is found (Kang et al., 2006). The calibration tool incorporated in
AVSWAT (ArcView SWAT) allows users to perform global changes
on input parameters that are commonly modified during the calibration process (Diluzio et al., 2001). Different scenarios should be
set in this tool to get an optimal result. In fact, not all parameters
present the same degree of sensitivity in the modeling. In the
parameter set, the initial SCS runoff curve number, CN2, the available water capacity of soil layers, SOL_AWC, and the soil evaporation compensation factor, ESCO, are the most sensitive
parameters for the modeled runoff (Immerzeel et al., 2008; Shen
et al., 2008). We also found that acceptable results could be obtained by mainly adjusting these three parameter values. In this
study, therefore, it is convenient to calibrate the model manually
based on a trial-and-error method. Certainly, other calibration
methods, including the calibration tool in AVSWAT, may be more
efficient (Neitsch et al., 2001; Muleta and Nicklow, 2005). In addition, the Stewart’s moisture stress yield reduction coefficient, Ky,
was also adjusted in the crop yield calibration process. After
achieving a satisfactory simulation for the runoff and crop yields
in the calibration period, the same modeling environment is applied in the validation period.
4.3.1. Runoff
The first 4 months (from January 1 to April 30, in 2005 and
2006) were used to warm up the model, and the subsequent
5 months (from May 1 to September 30) were used to calibrate
(in the year of 2005) or validate (in the year of 2006) the model.
Acceptable results were obtained by manually adjusting

parameters and the performance of the model was assessed according to the three indicators. As shown in Table 2, the Pearson coefficient (R2) and Nash–Sutcliffe criterion (Ens) reach 0.79 and 0.68 in
the calibration period. Furthermore, the two are above 0.90 and
0.83 respectively in the validation period. The absolute values of
relative error coefficient (RE) are less than 20% in the two periods.
The validation period exhibits better agreement than the calibration period probably because the model is more suitable for wetter
conditions and the data quality is better in the validation period.

In order to determine whether there were advantages in the
developed model, we performed a comparative simulation based
on the original edition of SWAT. The simulated conditions were
identical except that the fraction of potholes in HRUs was set as
0.90 to represent the fraction of paddy fields. As shown in Figs. 6
and 7, the performance of the original SWAT is not as good as
the developed model. In particular, there is significant underestimation of the peak flow processes. In contrast, the simulated daily
runoff series from the developed model correspond fairly well with
observed data, even though minor discrepancies still exist for the
peak flow simulation, for example at the final tillering stage (Table 1) when the drainage operation takes place in paddy fields.
The random and irregular drainage operations carried out by different farmers are difficult to account for so we have had to subjectively define that the drainage operations for all the paddy fields
were performed simultaneously. So these minor discrepancies
are unavoidable.
4.3.2. Crop yields
The 116 measured paddy fields were mainly distributed in eight
subbasins (Fig. 8). We aggregated these measurements to get effective statistics at the subbasin level at which the crop yields are estimated in the model. The results of calibration and simulation
periods were expressed together due to the small number of the
target subbasins (eight subbasins for both periods). Fig. 9 shows
a comparison of measurements and simulations for the paddy rice
yields. The Pearson coefficient (R2) is greater than 0.60, and the relative error coefficient (RE) is less than 5%. So the simulation results
roughly agree with the measurement data.
There are still some differences between the simulations and
the observations. Especially in the validation period, the simulated crop yields are relatively constant for the eight subbasins,
which is not consistent with the observations. These inconsistencies results from the limits of the Stewart model and the concept
formulation of HRUs. As described in Section 3.3, the Stewart
model only considers the impact from the total amount of
evapotranspiration and it fails to assess the influences of evapotranspiration processes at multiple growth stages. The crop
growth and yields are dynamically dependent on the evapotranspiration processes to some extent (Allen et al., 1998). Moreover,
the lumped concept of the HRU, which is a combination of a unique land use and a soil type, could degrade the simulation of
evapotranspiration and consequently spoil the crop yield

estimation.
4.4. Evaluation of water balance in paddy fields
It is important to test the water balance in paddy plots to evaluate the effects of the developed model. However, here we only

Table 2
Calibration and validation evaluations for daily runoff.
Item

Calibration
Validation

Period

2005 (May 1–September 30)
2006 (May 1–September 30)

Precipitation (mm)

384
529

Runoff (mm)

Ratio of runoff to precipitation

Obs.

Sim.

Obs.

Sim.

74
108

63
121

0.193
0.203

0.163
0.229

R2

RE (%)

Ens

0.79
0.90

À19.56
12.66

0.68
0.83

X. Xie, Y. Cui / Journal of Hydrology 396 (2011) 61–71

Runoff (mm/d)

(a) 10

0
30

8
6

Precipitation
Observation
Simulation

60

4

90

2

120

0
1-May

Precipitation (mm/d)

68

150
31-May

30-Jun

30-Jul

29-Aug

28-Sep

Date
0

8
6

30
Precipitation
Observation
Simulation

60

4

90

2

120

0
1-May

31-May

30-Jun

30-Jul

29-Aug

28-Sep

Precipitation (mm/d)

Runoff (mm/d)

(b) 10

150

Date
Fig. 6. Comparison of the observed and the simulated daily runoff hydrographs for the calibration period from May 1 to September 30 in 2005. The (a) and (b) are results from
the developed SWAT and the original SWAT respectively.

(a)14

30

Runoff (mm/d)

12
10
8
6

60

Precipitation
Observation
Simulation

90
120

4

150

2

180

0

1-May

210
31-May

30-Jun

30-Jul

29-Aug

Precipitation (mm/d)

0

28-Sep

Date

(b)14
Runoff (mm/d)

10

30

8
6

60

Precipitation
Observation
Simulation

90
120

4

150

2

180

0
1-May

31-May

30-Jun

30-Jul

29-Aug

28-Sep

Precipitation (mm/d)

0

12

210

Date
Fig. 7. Comparison of the observed and the simulated daily runoff hydrographs for the validation period from May 1 to September 30 in 2006. The (a) and (b) are results from
the developed SWAT and the original SWAT respectively.

perform analysis on the simulated results rather than compare
them with the observations, since the distributed paddy plots are
aggregated to an HRU in which their specified locations are not
preserved in SWAT. We have to pick water balance components
at the HRU level instead of the plot. An HRU with land cover of
the paddy rice is randomly picked out from the model, and its
water balance components were derived from both of the calibration and validation periods.

The water depth in paddy fields fluctuates in every day, while it
is restricted by the three critical depth (hmin $ hmax $ Hp) according
to the IIT scheme (Fig. 10, left and Table 1). When it exceeds the
maximum depth (Hp), a drainage operation is executed, for example on the June 10 in the calibration period (2005). Moreover, when
it reaches the minimum fitting depth (hmin), an irrigation operation
is performed, such as on the June 15 in the validation period
(2006).


69

X. Xie, Y. Cui / Journal of Hydrology 396 (2011) 61–71

transplanted, they generally increase until the booting and heading
stage and then decrease through to the harvest day (Fig. 10, right).
It should be noted that there are some singular points on the trendline at the final tillering stage in which no water is stored in the
fields with the practice of sun drying. Fig. 11 shows monthly water
balance components in paddy fields for the calibration and validation periods. The upper part refers to inflow components (In),
including precipitation and irrigation, and the lower part denotes
depletion components (Out), including evapotranspiration, surface
and groundwater discharges. The balance closures refer to the sum
of the net change, inflows plus depletions. We can see that there is
similar behavior in monthly water balance in the both periods. The
irrigation water, mostly from local ponds, is duly delivered to the
fields when required. Especially in May and June with a small
quantity of precipitation, a significant amount of irrigation water
from ponds and reservoirs is used to compensate the insufficient
precipitation, and there is no outflow to the main channels. In
addition, the total inflow (505 mm) is approximately equal to the
total depletion (510 mm) in the calibration period, while there is
a discrepancy in the validation period, 471 mm for the total inflow
and 537 mm for the depletion. In fact, this discrepancy will be offset by the uptake of the soil water.

2
1
3
4
17
7
5

6

8
10
9

12
15

11
14

13
16

Location for collecting
paddy rice yields

18
5. Discussion

Subbasin
Fig. 8. Measurement locations for the paddy rice yields.

Simulation (ton/ha)

9.0
Calibration
Validation
1:1 line

8.5

8.0

7.5

7.0
7.0

7.5

8.0

8.5

9.0

Measurement (ton/ha)
Fig. 9. Comparison of the measurement and the simulation of paddy rice yields.

Even though the evapotranspiration varies with the temperature and moisture conditions after the paddy seedlings have been

hmin
Hp

8

ET (mm/d)

100

Depth (mm)

Depth_Calibration
hmax

Depth_Validation

120

The developed SWAT model is capable of simulating the main
hydrological processes in irrigation areas. First, the nature of runoff
generation is accurately depicted. The paddy field can capture a
large amount of precipitation with a constant surface area in order
to keep water for the paddy rice growth. In the study area, the ratio
of runoff to precipitation is around 20% (Table 2), which means the
crop evapotranspiration and field storages account for most of the
volume of precipitation. Under these conditions, the simulated
runoff processes show good agreement with the observed values
and the developed model performs better than the original edition
of SWAT. Second, the crop yields are simulated with a simple
method. The simulated crop yields approximately agree with those
observed. This method is preferable to the EPIC model when the
data is poor. Third, the dynamic variation of water depth in paddy
fields is characterized well according to the three critical depths of
the specified irrigation schemes. Kang et al. (2006) also introduced
an outlet height for the pothole drainage, but the flexible operation
of the irrigation and drainage was not considered in their improvement. Furthermore, the water balance components correspond
well to the water requirement conditions of the paddy rice in different growing stages. Ponds and reservoirs, as local water sources,

play an important role for the timely irrigation that compensates
for the lack of water supply from canal transfers and precipitation
(Shahbaz et al., 2007b). These functions have been adequately represented in the model.

80
60
40

ET_Calibration

ET_Validation

6
4
2

20
0

0
1-May

31-May

30-Jun

30-Jul
Date

29-Aug

28-Sep

1-May

31-May

30-Jun

30-Jul

29-Aug

Date

Fig. 10. Water depth (left) and evapotranspiration (right) for the calibration and the validation periods in paddy fields.

28-Sep


70

X. Xie, Y. Cui / Journal of Hydrology 396 (2011) 61–71

400

400
300
200

Precipitation
Evapotransipiration
Blance closure

Irrigation

Evapotransipiration
Blance closure

Outflow

Blance components (mm)

Blance components (mm)

Precipitation

100
0
-100
-200
-300

300

Irrigation
Outflow

200
100

0
-100
-200
-300
-400

-400
May

June

July

August

May

June

July

August

Month

Month

Fig. 11. Monthly water balance in paddy fields for the calibration (left) and the validation periods (right).

Compared to the original edition of SWAT, the developed model

does not contain any special sensitive parameters, since no additional parameters were introduced except the response factor of
crop yields (Ky in Eq. (8)). When performing the parameter sensitivity analysis on this model, we also found that the three parameters, runoff curve number, CN2, the available water capacity of soil
layer, SOL_AWC, and the soil evaporation compensation factor,
ESCO, greatly influence the processes of runoff and evapotranspiration. This is in line with the conclusions of Muleta and Nicklow
(2005), Griensven et al. (2006) and Shen et al. (2008). Moreover,
the response factor of crop yields in Stewart model is a dominant
parameter, which has been widely explored when the model is
used to simulated crop yields (Allen et al., 1998; Li et al., 2003).
Therefore the sensitivity analysis is not investigated further here.
There are some aspects that deserve to be further research.
First, the groundwater simulation system should be developed.
Even though SWAT has its own module for the groundwater simulation, the model itself is lumped and therefore distributed parameters such as the hydraulic conductivity distributions are not
represented and thus the spatial distributions of the groundwater
level and the recharge rates are difficult to characterize (Kim
et al., 2008). Perhaps combining the SWAT and physically based
ground water models such as MODFLOW is an effective approach
(Sophocleous and Perkins, 2000; Sophocleous, 2005; Kim et al.,
2008). Second, the Stewart model for crop yield estimation is practical but its precision is limited. This is because the actual crop
yield depends on several inputs whereas the analysis here involves
only the water production function (Singh et al., 1999). As shown
in Fig. 9, the Pearson coefficient (R2) is just over 0.60. If the data
is easily collected, the EPIC model or other approaches, for example
the Jensen model (Jensen, 1968), may be a better alternative (Li
et al., 2003; Igbadun et al., 2007). Third, it is difficult to specify reasonable parameter values for ponds in SWAT, such as the principal
pond volume, since the number of ponds is often overwhelming
and their shapes are too irregular to be characterized in irrigation
areas but these parameters are significant for modeling. This can be
addressed by multi-period remote sensing to obtain reliable estimation of parameters.
Lastly, it should be noted that our developed SWAT model still
needs further verification and validation studies to construct a

comprehensive hydro-agronomic simulation tool. In this study,
only a 2-year data set is used for model testing, one for calibration
and the other for validation. In practice, this data set is not adequate and satisfactory for model tests, especially for long-term
hydrological simulations. So we are carrying out measurements
in Zhanghe Irrigation District and other irrigation areas. Nevertheless, based on the qualitative assessments and the water balance

analysis, the results appear to provide a reasonable representation
for the paddy fields in agricultural watersheds.
6. Conclusion
As a physically-based, comprehensive hydro-agronomic model,
SWAT is capable of accurately modeling hydrological processes
and crop growth in agricultural watersheds. But it fails to consider
the complicated water management conditions in paddy areas. In
this study, we performed improvements on this model. The paddy
field is assumed to occupy the whole area of an HRU which is the
basic computational unit in SWAT and the estimation of actual
evapotranspiration of paddy fields depends on two kinds of water
storage conditions. Moreover, a scheme of controlling irrigation is
introduced to this model with irrigation and drainage processes.
Specifically, three critical water depths are used to adjust the irrigation and drainage operations in paddy fields. Ponds and reservoirs, as local sources of water storage objects, can provide in a
timely manner water for paddy fields to compensate for canal
water transfers. In addition, a simplified model, the Stewart model,
is adopted to estimate crop yields.
We take Zhanghe Irrigation District (in China) as a demonstration area to test these developments. The simulated runoff exhibits
good agreement with the observed runoff in calibration and validation periods except for the stages when the drainage is carried out.
These results also indicate that the developed model is preferable
to the original edition of SWAT for paddy rice areas. The estimates
of rice yields are also acceptable in both of the periods. Moreover,
the water balance components, including the daily water depth,
actual evapotranspiration and the monthly water balance closures,

reasonably represent the actual conditions for paddy fields.
Consequently, the improved framework is flexible and practical
and each of the components can be regarded as an improvement to
SWAT in simulating the hydrological processes in irrigation districts where paddy rice is planted. Ongoing work is oriented towards improving the groundwater simulation and further testing
of the performance of the model with more data sets from different
paddy rice areas. All these efforts will help to assess the influences
from human activities in the agricultural watershed, such as irrigation schemes and crop planting distribution. This new tool can also
be used to examine the productivity at different scales in agricultural water management.
Acknowledgements
This study was partially supported by grants from the National
Natural Science Foundation of China (No. 50879060/50839002)


X. Xie, Y. Cui / Journal of Hydrology 396 (2011) 61–71

and the China Postdoctoral Science Foundation (No.
20080440271). We are grateful to the anonymous reviewers for
constructive comments. Particular acknowledgement goes to one
of the associate editors giving language improvement. We also
would like to thank Dr. Liangsheng Shi for his helpful review on
the original manuscript.
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