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

Journal of Hydrology 396 (2011) 61–71 Contents lists available at ScienceDirect 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 December 2008 Received in revised form 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 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: xiexh@pku.edu.cn (X Xie), Cuiyuanlai@263.net (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 62 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 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 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 63 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 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 Note: SA is the surface area of the water body, ha; V is its volume , m ; H is the depth , m; and the slp is the average slop of a specified HRU Fig 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 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 64 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 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 ẳ Ecan ỵ Ep ỵ Es 2aị (1) If the paddy field is in a wet state and the HRU is impounded, then ET act ẳ Ecan ỵ 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 ẳ ST i1 ỵ P i ỵ IRi 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 4aị IRi ẳ 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 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 10–30–50 Tillering stage Booting stage Wet Dry 6/3–7/2 30 10–40–60 7/3–7/9 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 10–40–60 Ripening stage Wet Dry 8/14–8/21 0–20–30 8/22–8/29 0–0–0 65 X Xie, Y Cui / Journal of Hydrology 396 (2011) 61–71 DRi ¼ ST i À Hi;p if ST i > Hi;p 5aị DRi ẳ if ST i 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 ẳ V i1 ỵ V i;pcp ỵ 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 (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 ị SA V i;irr ẳ V i ; V i;irr ẳ 0; if ST i < hi;min 7aị and V i 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 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) 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 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 66 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 Location of the demonstrational area of ZID Fig 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 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 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 represents perfect correlation, while a value of 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 Mi Oi ị2 Ens ẳ Pniẳ1 iẳ1 Oi hOi iị 10ị 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 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 months (from January to April 30, in 2005 and 2006) were used to warm up the model, and the subsequent months (from May 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 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 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 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 30 Precipitation Observation Simulation 60 90 120 1-May Precipitation (mm/d) 68 150 31-May 30-Jun 30-Jul 29-Aug 28-Sep Date 30 Precipitation Observation Simulation 60 90 120 1-May 31-May 30-Jun 30-Jul 29-Aug 28-Sep Precipitation (mm/d) Runoff (mm/d) (b) 10 150 Date Fig Comparison of the observed and the simulated daily runoff hydrographs for the calibration period from May 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 60 Precipitation Observation Simulation 90 120 150 180 1-May 210 31-May 30-Jun 30-Jul 29-Aug Precipitation (mm/d) 28-Sep Date (b)14 Runoff (mm/d) 10 30 60 Precipitation Observation Simulation 90 120 150 180 1-May 31-May 30-Jun 30-Jul 29-Aug 28-Sep Precipitation (mm/d) 12 210 Date Fig Comparison of the observed and the simulated daily runoff hydrographs for the validation period from May 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 17 10 12 15 11 14 13 16 Location for collecting paddy rice yields 18 Discussion Subbasin Fig 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 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 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 20 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 -100 -200 -300 300 Irrigation Outflow 200 100 -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 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 References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998 Crop Evapotranspiration Guidelines for Computing Crop Water Requirements (FAO Irrigation and drainage paper 56) Food and Agriculture Organization, Rome Anbumozhi, V., Yamaji, E., Tabuchi, T., 1998 Rice crop growth and yield as influenced by changes in ponding water depth, water regime and fertigation level Agric Water Manage 37, 241–253 Anuraga, T.S., Ruiz, L., Kumar, M.S.M., Sekhar, M.c., Leijnse, A., 2006 Estimating groundwater recharge 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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. .. 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 in ltration,