Runoff simulation in Donghe Basin using SWAT model Nai-Jia Guo, Lin-Lin Xiao, Qing-Kai Sheng Collage of Recourse and Environment, Southwest University Chongqing 400716, China e-mail:
[email protected]
Abstract—Most studies and applications based on SWAT model were in monthly or yearly time step. This study made an attempt to validate the adaptability of runoff simulation in the Three Gorges Reservoir areas in daily time step. By using the soil map (1:50,000), land-use map (1:50,000) in Kaixian, and weather data at Wanzhou station, the related databases were first constructed, and then a simulation was made. The result was evaluated by Nash-Sutcliffe (Ens) and correlation coefficient (r). Using flow data at Wenquan station, the model was calibrated with Ens (0.98), r (0.99). The values of Ens (0.91) and r (0.98) in validation period indicate the simulation result is acceptable and demonstrate that the SWAT model could get a good simulation in daily as well as in monthly time step under some conditions.
Hong-Bin Liu* Chongqing Key Laboratory of Digital Agriculture Chongqing 400716, China *Corresponding author:
[email protected]
II.
MATERIALS AND METHODS
A. Study area Donghe basin, covering 1034km2, is located in the northeast of KaiXian, Chongqing, and on the north shore of the Three Gorges areas (Fig.1a). The topography of the area is hilly and mountainous with a range of altitude of 185-2553m. The climate is moderate sub-tropic with an annual precipitation of 1100-1500mm and average annual temperature of 18.6℃.The predominant soil parent materials are purple sandy shale and purple sandy rock.
Index Terms— SWAT,the Three Gorges Reservoir, DEM
(a) I.
INTRODUCTION
Soil and Water Assessment Tool (SWAT) has been a successful and widely used model. It is widely used in water resources management under different conditions abroad (Arnold and Allen, 1996; Bingner, 1996; Peterson JR and Hamlet JM, 1997). For example, Behera and Panda used SWAT model to assess the effects of different management measures on runoff, sediment loads and nutrient concentration in Midnapore watershed. They tried to find the Best Management Practices (BMPs).The model successfully simulated runoff and water quality, and one of the best management practices from the 48 sets of simulation management programs was selected [1]. In China the studies of SWAT and its applications are very popular too, many researches have been made [2-8]. However, most of the studies made their simulation in monthly time step.
(b)
The main objective of this paper was to validate the adaptability of runoff simulation in the Three Gorges areas in daily time step. This study was carried out in three successive steps: spatial and non-spatial databases were first constructed. The databases were then used to drive the model with inputs to calibrate the parameters of the model. The aim of the second step was to quantify the predictive quality of the model in this study area. The third step was to test the applicability of the model in the Three Gorges Region in daily time step. Figure 1. Location and digital elevation model (DEM) of the study area
B. Data collection In this study, several kinds of materials are used to run the SWAT model. Data used by the model were: Soil map of Donghe basin with a scale of 1:50,000 Land use map of Donghe basin with a scale of 1:50,000 Digital Elevation Model (DEM) of Chongqing with a scale of 1:250,000 Meteorological data Hydrological data Soil property data In all of these data, the daily meteorological data at Wanzhou station from 1955 to 2000 are used to set the weather generator parameters. The daily precipitation data and the flow data from May to September of 2004 at Kaixian WenQuan hydrological station are used to initialize calibrate and validate the model. The soil property data according to the soil records in Sichuan province are used to help build up the soil database. C. Stream network extraction DEM is the foundational material of spatial analysis and runoff simulation. To get the DEM of the study area, two steps were taken: Project the original one into the WGS_1984_UTM_Zone_48N coordinate system and then clip it by a mask, the result is shown in Fig.1b. This DEM may have many sinks and the presence of sinks may result in an erroneous flow–direction raster. So it was filled by ArcSWAT. By setting a certain threshold ArcSWAT can automatically extract the stream network. The detail level of the network can be controlled by adjusting the threshold of upstream catchment area. In this study, an initial threshold value was set at the beginning, by comparing the differences between the generated and the actual river channel the threshold was adjusted step by step. The threshold value was defined referring to Zhao’s study [9].
Figure 2. Land use types of the study area
E. Soil database construction Based on the soil map of Donghe basin (1:50,000), there are 66 soil species in the study area. After taking the needs of SWAT model and the proportion of each type into consideration, these types were reclassified into 21 categories (Fig.3).
D. Land-use reclassification According to the requirement of SWAT model, land use types were reclassified into agricultural land (AGRL), gardening (FRSD), grass (PAST), urban land (URBN), river (WATR), bare ground (BARE) 6 types. The results were shown in Table Ⅰ and Fig.2. TABLE I.
CODE DESIGNED FOR LAND USE
Land use Paddy field Non-irrigated farmland Garden Forest Grass Factory Urban Bare ground Water
Code AGRL AGRL FRSD FRSD PAST URBN URBN BARE WATR
Figure 3. Soil types of the study area
After that, a conversion of different soils texture system was made by using the cubic spline interpolation method [10]. Parameters, such as Wilting Point, Field Capacity were calculated using the SPAW software [11-12]. And USLE_K (USLE equation soil erodibility factor) was determined by an equation which was proposed by Williams in 1995[13]. F. Weather parameters determination Weather generator is an indispensable part of the SWAT model. There are 5 kinds of parameters needed by it: precipitation-related, temperature-related, humidity-related, wind-related, solar-related parameters. The precipitationrelated parameters such as PCPSTD (standard deviation for daily precipitation in month), PCPSKW (skew coefficient for daily precipitation in month), were calculated from the observation using software pcpSATA.exe. DEWPT (average daily dew point temperature in month) was calculated with the software DEW.exe based on the daily relative humidity data. And SOLARAV (average daily solar radiation for month) is calculated in the same method used in PangJingpeng’s research [14]. All these variables are calculated based on data of Donghe basin. G. Set up related variables
The subbasin division were shown in Fig.4, 28 subbasins were generated. The basin was divided into 28 HRUs by setting the thresholds level for the land use category, soil type, and slope class to 45%. In this study, precipitation data were recorded in Kaixian, WenQuan station. Other weather data, such as temperature, humidity, were provided by WanZhou station. Hydrological data were observed during May to September in 2004 and were divided into 3 phases, from May to August were used for initialization, from September 1 to 15 for calibration and from 16 to 30 for validation.
Nash-Sutcliffe could be calculated as follow: n
E ns 1
(Q i 1 n
o
Qc ) 2 (1)
(Qo Qo ) 2 i 1
where Qo is the flow data observed in one day, Qc is the flow data in one day simulated by the SWAT model, Q o is the average of observed flow data in a month. III.
RESULTS AND DISCUSSION
A. Model calibration There are many parameters have influence on the runoff simulation based on SWAT model. Among them, curve number (CN), soil evaporation compensation factor (ESCO), available water capacity of the soil layer (SOL — AWC), groundwater "revap" coefficient (GW—REVAP), base flow alpha factor (ALPHA—BF) are important parameters. Using the observed flow data in September 1 to 15 of 2004 to calibrate the model, and the result is shown in Fig.5 .Related Nash-Sutcliffe, relative error and correlation coefficient are listed in the Table Ⅱ. Good agreement was found between the observations and simulations with r of 0.99, Ens of 0.98, and Re of -9.3%. TABLE II.
THE RESULTS OF CALIBRATION
Average flow（m3/s） Simulated
Observed
188
207.14
Re(%) -9.3
R
Ens
0.99
0.98
Figure 5. Runoff in calibration period of days
Figure 4. Sub-basins of Donghe basin
H. Model evaluation Nash-Sutcliffe (Ens), relative error (Re) and correlation coefficient (r) are employed to evaluate the adaption of the model.
B. Model validation When the model was calibrated, data from September 16 to 30 of 2004 were used to validate the model. The evaluation results are shown in Fig.6 and Table Ⅲ.
TABLE III.
THE RESULTS OF VALIDATION
Average flow（m3/s） Simulated
Observed
56
64.82
REFERENCES [1]
Re(%) -13.6
R
Ens
0.98
0.91 [2]
[3]
[4]
[5]
[6]
[7] Figure 6. Runoff in validation period of days
The values of Nash-Sutcliffe (0.91) and correlation coefficient (0.98) showed close agreement between simulated and observed runoff in the study area. The results indicated that the SWAT model could be used to simulate the runoff in daily time step under the tested conditions. Most studies suggest that the simulation results are much better with a time-step in month than in day. However, the results of our study and Wang et al. who applied the SWAT model to simulate daily runoff in Heihe basin (10009 km2) and the Nash-Sutcliffe of their study was 0.83[15] demonstrated that the SWAT model could get an acceptable simulation in daily as well as in monthly time step. IV.
[8]
[9]
[10]
[11]
CONCLUSION
The current study made an attempt to validate the adaptability of runoff simulation using SWAT model in the Three Gorges areas in daily time step. The acceptable result indicates that the SWAT model could simulate runoff in daily time step in this region during a rainy season. So, it is important and meaningful to enhance the applicability of SWAT model in this area. Meanwhile it is necessary to establish related soil, land use databases, climate database, and hydrological information database. All these databases should provide foundational materials for this model. Future research is needed to apply this model to different locations in order to be able to make practical decisions with respect to water resources management and conservation.
[12]
[13]
[14]
[15]
Behera, S., Panda R.K., “Evaluation of management alternatives for an agricultural watershed in a sub-humid subtropical region using a physical process based model, ” Agriculture Ecosystems & Environment,vol.113, pp.62 - 72, 2006. Li Jia-ke, Liu Jian, Qin Yao-min, QIN Yao-min,LI Huai-en, “Distributed simulation on nitrogen non-point source pollution in the Weihe River watershed based on SWAT model,” Journal of Xi’an University of Technology,vol.24(3), pp.278-285, 2008.(in Chinese) Zhu Xinjun, Wang Zhonggen, Li Jiaxin,Yu Lei,Wang Jingui, “Applications of SWAT model in Zhang Wei River Basin.” Progress in Geography, vol.25(5), pp.106-110, 2006. (in Chinese) Li Dan, Xue Lianqing, Hao Zhenchun, “SWAT simulation of effect of stream water quality on non-point source pollution,” Environmental Pollution & Control, vol.30 (30), pp.4-7, 2008. Zhenyao Shen, Qian Hong, Hong Yu, Ruimin Liu, “Parameter uncertainty analysis of the non-point source pollution in the Daning River watershed of the Three Gorges Reservoir Region, China,”.Science of the total environment, vol.405, pp.195-205, 2008. Z.Y. Shen, Y.W. Gong, Y.H. Li, Q. Hong, L. Xu, R.M. Liu,“A comparison of WEPP and SWAT for modeling soil erosion of the Zhangjiachong Watershed in the Three Gorges Reservoir Area,” Agricultural Water Management vol.96, pp.1435-1442, 2009. Zhenyao Shen, Yongwei Gong, Yanhong Li, Ruimin Liu, “Analysis and modeling of soil conservation measures in the Three Gorges Reservoir Area in China,”Catena, vol.81,pp.104112, 2010. Zhenyao Shen, Qian Hong, Hong Yu, Jun-feng Niu, “Parameter uncertainty analysis of non-point source pollution from different land use types,” Science of the total environment, vol.408, pp.1971-1978, 2010. Zhao Ya-ping, Huang Yan, Qiu Dao-chi, “Application and study of extracting digital stream network from elevation model,”Journal of Xinyang Normal University, vol.21(2), pp.232-235, 2008.(in Chinese) Cai Yong-ming, Zhang Ke-li, Li Shuangcai, “The study on the conversion of different soils texture,” Acta Pedologica Sinica,vol. 40(4), pp.511-517, 2003. (in Chinese) K.E Saxton, W.J. Rawls, J.S. Romberger,R.I. Papendick, “Estimating generalized soil-water characteristics from texture,” Soil Science Society of America Journal, vol.50(4), pp.10311036,1986. Saxton, K.E., Rawls, W.J.,“Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions,” http://users.adelphia.net/~ksaxton/SPAW%20Download.htm /200609 Sliarply, A.N., Williams, J.R., EPIC-Erosion/Productivity Impact Calculator: l Model Documentation. USDA Technical Bunefin,1990, NO.1768. Pang Jing-peng, Xu Zong-xue, Liu Chang-ming, “Weather generator and database in the SWAT model,” Journal of China Hydrology,vol. 27(5), pp. 25-30, 2007. Wang Zhong-gen, Liu Chang-ming, Huang You-bo, “The theory of SWAT model and its application in Heihe Basin.” Progress in Geography, vol.22(1),pp.79-86, 2003. (in Chinese)
