Tài liệu Application of swat model in the upstream

CHINESE GEOGRAPHICAL SCIENCE Volume 13, Number 4, pp. 334-339, 2003 Science Press, Beijing, China APPLICATION OF SWAT MODEL IN THE UPSTREAM WATERSHED OF THE LUOHE RIVER ZHANG Xue-song, HAO Fang-hua, CHENG Hong-guang, LI Dao-feng (State Key Laboratory of Water Environment Simulation, Institute of Environmental Sciences, Beijing Normal University, Beijing 100875, P. R. China) ABSTRACT: In the Huanghe (Yellow) River basin, soil erosion is a serious problem, while runoff and sediment yield simulation has not been extensively studied on the basis of GIS (Geographic Information System) and distributed hydrological model. GIS-based SWAT (Soil and Water Assessment Tool) model was used to simulate runoff and sediment in the Huanghe River basin. The objective of this paper is to examine the applicability of SWAT model in a large river basin with high sediment runoff moduius, which could reach 770t/(krn2-a). A two-stage "Brute Force" optimization procedure was used to calibrate the parameters with the observed monthly flow and sediment data from 1992 to 1997, and with input parameters set during the calibration process without any change the model was validated with 1998-1999's observed data. Coefficient of examination (R2) and Nash-Suttcliffe simulation efficiency (E,s) were used to evaluate model prediction. The evaluation coefficients for simulated flow and sediment, and observed flow and sediment were all above 0.7, which shows that SWAT model could be a useful tool for water resources and soil conservation planning in the Huanghe River basin. KEY WORDS: Luohe River; watershed; SWAT model; sediment; flow CLC number: S 1 5 7 Document code: A Article ID: 1002-0063(2003)04-0334-06 1 INTRODUCTION do some researches on flow and sediment modeling. In the Huanghe (Yellow) River basin, soil erosion is a serious problem, while runoff and sediment yield simulation has not been extensively studied on the basis o f GIS (Geographic Information System) and distributed hydrological model. In this study, the Lushi watershed, which is located at the upstream o f the Lushi Hydrological Station in the Luohe River--the biggest" tributary o f the Huanghe River and downstream o f Xiaolangdi Dam, is selected as the study area. The level o f soil erosion in Lushi watershed is moderate in the Huanghe River basin: the rate o f soil erosion is about 2000-4000t/(km2-a) and sediment runoff modulus reaches 770t/(km2.a) (GUO and ZHENG, 1995). SWAT (Soil and Water Assessment Tool) has been extensively validated across the U.S. for stream flow and sediment yields (ARNOLD and ALLEN, 1999), and here the GIS-based SWAT model was selected to 2 MATERIALS AND METHODS 2.1 SWAT Model Description SWAT is a hydrologic/water quality model developed by United States Department o f Agriculture-Agricultural Research Service (USDA-ARS) (ARNOLD et d . , 1998). It is a continuous time model that operates on a daily time step. The objective o f the model is to predict the impact o f management on water, sediment, and agricultural chemicals in large ungaged basins. To satisfy the objective, this model is physically based (calibration is possible on ungaged basins); uses readily available inputs; is computationally efficient to operate on large basins in a reasonable time; and is continuous in time and capable o f simulating in long period for computing the effects o f management changes. The sub-basin/sub-watershed components o f SWAT Received date: 2003-04-28 Foundation item: Under the auspices of the Key Program of National Natural Science Foundation of China (No.50239010) and Ph. D. foundation from Ministry of Education (No.20010027013) Biography: ZHANG Xue-song (1979-), male, a native of Liaocheng City, Shandong Province, Master candidate of the Institute of Environmental Sciences, Beijing Normal University, specialized in water resources and environmental modeling Application of SWAT Model in the Upstream Watershed of the Luohe River SPCON is the concentration in g/m 3 at a velocity of lm/s; V is flow velocity in m/s; and SPEXP is a can be placed into eight major components: hydrology, weather, erosion/sedimentation, soil temperature, plant growth, nutrients, pesticides, and land management. SWAT uses a modified version of the SCSCN (Soil Conversation Service Curve Number)method for predicting runoff (USDA-SCS, 1972). Erosion and sediment yield are estimated for each sub-basin with the Modified Universal Soil Loss Equation (MUSLE) (WILLIAMS, 1975). The channel sediment routing equation uses a modification of BAGNOLD's sediment transport equation (BAGNOLD, 1977) that estimates the transport concentration capacity as a function of velocity. CY,~:SPCONx Vsm~e constant in BAGNOLD's equation. The sediment deposit and re-suspension can be calculated according to the sediment load entering the channel. 2.2 Watershed Description SWAT model was applied to the 4623km 2 of the upstream watershed of the Luohe River (Fig. 1). This area is mainly mountainous, and the Qinling Mountains is located to the south of the watershed, Huashan Mountain and Yao Mountain to the north. The degree of slope in this area is normally about 30 degree. This area belongs to warm temperate zone, and precipitation is abundant. where CY,, is sediment transport concentration in g/m3; ll0°]00'E [ I , ." 110°~4' E ..,¢" " ~ "°~" " " "~o" tA , . ~ h ~ , oN w I Mr ~" -- k ~ 2 h a n g r f i l d i - ~ - - - ~ ~'~ m - ~ ~! ~l~himenyu N - t.uoyuan ."~ff~_ ~% . ~ - - . ~ " ~ ~ ~ B o y u ~ i "-,¢ ~ ~ "~ "" " ~_ m i .. Manli 2 lz.,- 110°48' E .1- -,~ol. ...,~,'. t-- s 335 ~ Raingauge - I ___ Watershed boundary Water System t II0°~I0'E - ~ " - !, I '-, i .......... 31-1Miaot~" '~-. .~.,..i / ~ - ~ , , ~ iLiugua n "~ I Hao in h e ; [1 X "-" P g ! 1, ~ IPanhe Shahejie "~1! ~ f u t ° n g g ° u ~ l ~ ~ ~- ipLushi |' ~ ! k. ~_ Jianbei "-'l~.a, °~ "- I ~nttyn,a -" ""-~ ~ i ( "~ ~ | t~ Nianzigoukoff'.,. ~'u lLCruanDo m ~ "i.... . . . . . ,,..~.@ongxia " ~ 1 ~ i " "X I i :.. ~ t m i ' ' " Zulmyu._.~ m -¢ 110°24'E 110°48'E Fig. 1 Map of the study area Land-uses in this watershed are mostly forest and cropland in the upper portion while cropland and pasture are wide spread in the lower portion. Major soil series are zonal from the low elevation to the high elevation area, accordingly the soil series change from Calcic Cinnamon Soils, Typic Cinnamon Soils, Typic Burozems to Clay Pan Yellow-brown Earths, the percentage of which in total watershed are about 27%, 34%, 38% and 1%, respectively. 2.3 Model Inputs for Lushi Watershed A Geographic Resource Analysis Support System-Geographic Information System (GRASS-GIS) interface (SRINIVASAN and ARNOLD, 1994) was used to develop SWAT input files for the watershed (Table 1). Initially, the watershed was delineated into sub-basins by using the digital elevation map. The delineated sub-basin map, land-use and soil map were overlaid. SWAT simulates different land-uses in each sub-basin. Table 1 Data sources for Lushi watershed Data type Topography Scale 1:250000 Data description/properties Elevation, overland, and channel slopes, lengths Soil 1:4000000 Soil classifications Land use 1: 100000 Land-use classifications Weather Daily precipitation Land management Tillage, information ing dates for different crops planting and harvest- 336 ZHANG Xue-sor~g, HAO Fang-hua, CHENG Hong-guang et ol. 2.4 Evaluation of Model Prediction Mean, standard deviation, coefficient of determination (R2), and Nash-Suttcliffe simulation efficiency (E,~) (NASH and SUTTCLIFFE, 1970) are used to evaluate model prediction. The R 2 value is an indicator of strength of relationship between the observed and simulated values. Nash-Suttcliffe simulation efficiency (Ens) indicates how well the plot of observed versus simulated value fits the 1:1 line. If the R 2, Ens values are less than or very close to 0, the model prediction is considered "unacceptable or poor". If the values are 1, then the model prediction is "perfect". However there are no explicit standards specified for assessing the model prediction using these statistics. 2.5 Model Calibration The SWAT model was built with state-of-the-earth components with an attempt to simulate the processes physically and realistically. Most of the model inputs are based on readily available information. It is important to understand that SWAT is not a "parametric model" with a formal optimization procedure (as part of the calibration process) to fit any data. Instead, a few important variables that are not well defined physically such as runoff curve number and Universal Soil Loss Equation's cover and management factor, or C factor may be adjusted to provide a better fit. A two-stage "Brute Force" optimization procedure described by ALLRED and HAAN (1996) was used to find the optimum parameter values. In the first optimization stage, a rough estimate of the optimum parameter set was obtained by setting a percentage by which each parameter was to be changed. Eight increments or decrements were performed for each parameter. Curve numbers were always increased or decreased by a whole number. If the optimum values of parameters were obtained at the upper or lower boundary of the parameter values, the step sizes of the parameter values were increased and the same procedure was repeated to insure that the optimum parameter estimates did not fall at the boundary values. Mathematically, the optimum parameter value can be represented as (P~)j, where P, is the average optimum value of parameter i obtained at step j (j" -- 1, 2, ..., 8). I f j was equal to t or 8, then the range of the step size was increased, and the optimization procedure was repeated. The second optimization was conducted in a similar manner as the first one by further refining the parameter values. Refinement was accomplished by using a much narrower range of parameters obtained from the first optimization. If the optimum parameter obtained by the first approximation was (Pi)i, then the range of the parameters in the second optimization was (Pi)i-I to (Pi)i+l. In the second optimization, a step size in the form of a fraction for each parameter was calculated and divided the range of the parameter into 10 evenly distributed values. Each parameter was then increased or decreased by this fraction and model runs were performed. The set of parameters that match the observed data best was considered as the final optimum parameter set. 2.5.1 Calibration procedure There are numerous potential errors that can occur in the measured input data and data used for calibration, including: 1) spatial variability errors in rainfall, soils and land-use; 2) errors in measuring flow; 3) errors caused by sampling strategies. WINTER (1981) suggested error rates in annual estimates of precipitation, stream flow, and evaporation ranged from 2% to 15%, whereas monthly rates could range from 2% to 30%. Errors in sampling strategies can also be significant. WALLING and WEBB (1988) determined that using continuous turbidity and daily flow data resulted in errors of 23% to 83% when calculating annual sediment loads. The calibration criteria for this study evolved based on these potential errors shown in literature. Initially, base flow was separated from surface flow for both observed and simulated stream flows using an automated digital filter technique (NATHAN and MCMAHON, 1990; ARNOLD and ALLEN, 1999). Calibration parameters for various model outputs were constrained within the ranges shown in Table 2. Model outputs were calibrated to fall within a percentage of average measured values and then monthly regression statistics (R 2 and End) were evaluated. If measured and simulated means met the calibration criteria and monthly R 2 and En~ or did not, then additional checking was performed to ensure that rainfall variability and plant growing seasons were properly simulated over time. If all parameters were pushed to the limit of their ranges for a model output (i.e. flow or sediment) and the calibration criteria were still not met, then calibration was stopped for that output. The procedure for calibrating the SWAT model in flow, sediment is shown in Fig.2 (SANTHI et al., 2001 ) . Stream flow was the first output calibrated (Fig. 2). Surface runoff was calibrated until the difference between average measured and simulated surface runoffs was within 15% and monthly R 2>~0.6 and Ens >I Application of SWATModel in the Upstream Watershedof the LuoheRiver 337 Table 2 Inputsused in model calibration Variable Process Description CN REVA PC ESCO EPCO C factor Flow Flow Flow Flow Sediment Curve number Ground water re-evaporation coefficient Soil evaporation compensation factor Plant uptake compensation factor Cover or management factor Parameter range +8 0.00 to 0.00 to 0.00 to 0.003 to SPCON SPEXP Sediment Sediment Linear factor for channel sediment routing Sediment exponential factor for channel sediment routing 0.0001 to 0.01 1.0 to 1.5 Separatesurfacerunoff(SR)and base flow (BF) for measureddaily flow [ + [ I" .... llf~ Yes {" [-"'-\ . . . . . g. . . . . . . . dand . . . . . ge simulatedBF<15%,e~>~O~, [ Run SWAT "~N_~.2_~Adjast~EYXPC, }| ESCO,ePCO Yes/ averagemeasuredandaverage "~No [ AdjustCfactor I V"-~X simu~tedSediment<20%,i~>0.SJ--[ SPCON,SPE'~ [ ~' Calibrationcomplete [ E~: Nash-Suncliffeefficiency R~:Coefficientof determination Fig. 2 Calibrationprocedure for flow and sediment in SWAT model 1.00 1.00 1.00 0.45 Value/change _+2 0.10 0.20 0.10 Pasture: 0.006 Forest: 0.010 Cropland: 0.20 0.0008 1.0 pensation factor (EPCO) were adjusted from SWAT initial estimates to match the simulated and observed flows (Table 2). The simulation was started from 1991 to reduce errors in initial estimates of state variables, such as soil water content and surface residue. 2.5.3 Sediment The cover, or C factor, of the Universal Soil Loss Equation was adjusted to match observed and simulated sediment loads (Fig. 2). The C factor was adjusted (Table 2) to represent the surface better in the range and pasture lands. Channel sediment routing variables such as the linear factor for calculating the maximum amount of sediment re-entrained during channel sediment routing (SPCON) and the exponential factor for calculating the sediment re-entrained in the channel sediment routing (SPEXP) were also adjusted (Table 2) during the sediment calibration. These two variables were adjusted to represent the cohesive nature of the channels in this watershed (ALLEN et al., 1999). 2.6 Model Validation 0.5. The same criteria were applied to base flow, and surface runoff was continually rechecked as the base flow calibration variables also affect surface runoff. Sediment was calibrated after flow calibration and continued until the difference between average measured and simulated sediment loads was within 20%. 2.5.2 Flow SWAT simulation was calibrated (Fig. 2) for the period from 1992 to 1997. For flow calibration, the runoff curve numbers (CN) were adjusted within +8 from the tabulated curve numbers (MOCKUS, 1969) to reflect conservation tillage practices and soil residue cover conditions of the watershed (Table 2). Other related model parameters such as re-evaporation coefficient for ground water [REVAPC represents the water that moves from the shallow aquifer back to the soil profile/root zone and plant uptaken from deep roots (ARNOLD et al., 1993)], soil evaporation compensation factor (ESCO), and plant evaporation com- In the validation process, the model was operated with input parameters set during the calibration process without any change and the results were compared to the remaining observational data to evaluate the model prediction. Measurements from January 1998 to December 1999 were used to validate the model for Lushi Hydrological Station. The same statistical measures were used to assess the model prediction. 3 RESULTS AND DISCUSSION 3.1 Model Calibration 3.1.1 Flow Monthly measured and simulated flows at Lushi Hydrological Station matched well (Fig. 3a). Means and standard deviations of the observed and simulated flows were within a difference of 15 % (Table 3). Fur- ZHANG Xue-song, 338 HAO Fang-hua, CHENG Hong-guang et ol. (a) flow in calibration period II Observed - (b) sediment in calibration period Simulated $ 150 % 1500 100 ~ tooo o~ ~x 50 ~ 500 0 ~ 0 l 5 9 131721252933374145495357616569 Observed ---II--Simulated 1 5 9 131721252933374145495357616569 Month (1992-1997) Month (1992-1997) (There were 72 months from 1992 to 1997, 1 represents January 1992, 2 February 1992, and so on and so forth.) Fig. 3 Observedand simulatedmonthlyflow and sedimentloadings during calibrationperiod ther agreement between observed and simulated flows is shown by the coefficient of determinations and E,s greater than 0.8 (Table 3). The estimated proportion of base flow from the observed flows was 35% by the filter technique, which was 32 % for the same locations for SWAT simulated flows. These results for surface runoff and base flow for observed and simulated flows reveal that hydrologic processes in SWAT are modeled realistically and the concentrations at flow regime are realistic. 3.1.2 Sediment The temporal variations of sediment loading at Lushi Station are represented in Fig. 3b. Means and standard deviations of observed and simulated sediment were both within a difference of 20% (Table 3). The values of R 2 and Ens were both 0.70 (Table 3) which indicates that the simulated sediment is closer to the observed sediment and this model is able to predict sediment loadings well. 3.2 Model Validation Table 3 Monthly calibration results at Lushi Hydrological Station from 1992 to 1997 Variable Mean Standard deviation Flow volume (m3/s) Sediment (x 103 t) 13.15 12.29 24.83 21.23 0.87 0.86 800.5 880.7 2390.6 2080.6 0,70 0.70 Observed Simulated Observed Simulated Rz E,s 3.2.1 Flow Observed and simulated flows at Lushi Hydrological Station matched well (Fig. 4a). The model overpredicted the flow in of June, September and October 1998, April, May, and in late 1999, and slightly underpredicted in August, December 1998 (Fig. 4a). The difference might be due to the spatial variability of (a) flow in validationperiod $ Observed (b) sediment in validation period --II.--Simulated 200 [ ~ °15ot $ Observed 3 5 +Simulated 2500 F i 2°°° 15o0 ~lOO 1000 I~ 50 0 500 0 1 3 5 7 9 11 13 15 17 19 21 23 Month (1998-1999) 1 7 ......... l--A--__-9 11 13 15 17 19 21 23 Month (1998-1999) (There were 24 months from 1998 to 1999, 1 represents January 1998, 2 February 1998, and so on and so forth.) Fig. 4 O b s e r v e d a n d s i m u l a t e d m o n t h l y f l o w a n d s e d i m e n t l o a d i n g s d u r i n g v a l i d a t i o n p e r i o d precipitation. However, the prediction statistics was high (Table 4). Table 4 Monthlyvalidation results at Lushi Hydrological Station from 1998 to 1999 Variable Mean Observed Simulated Standard deviation Observed Simulated R2 En~ Flow volume (m3/s) Sediment (x 104 t) 15.45 20.73 157.70 134.10 0.84 0.81 320.77 320.30 4320.70 3460.70 0.98 0.94 3.2.2 Sedimer~t Observed and simulated sediment loading matched well. However, the model underpredicated the sediment in May and August 1998, and slightly overpredicated the sediment in July 1999 (Fig. 4b). The values o f R 2 and Ens are both above 0.9, which indicates that the model is able to predict sediment reasonably. The high values of R 2 and E,s may be due to that in 1998 the sediment yield was much greater than sediment yield in 1999. So as the "goodness of fit" of observed Application of SWAT Model in the Upstream Watershed of the Luohe River and simulated data in 1998 was good, the results were agreeable even though in 1999 the data did not match well. Another reason was that the observed data were not accurate due to the difficulties to measure sediment. 4 CONCLUSIONS The basic spatial and attribute database for runoff and sediment modeling in the study area has been established using the GIS technology. The objective of this study was to calibrate and validate SWAT model in a watershed with high sediment loading. Monthly simulated flow, and sediment loadings were compared with observed values for the calibration and validation periods. The results show that in most instances, simulated flow, and sediment were close to the measured values during the calibration period and validation period. In general, SWAT predictions were acceptable. And due to its spatial analyst capability, GIS-based SWAT model could be used as a useful tool for planning and management of water resources systems in a manner that is environmentally sustainable and socially acceptable. REFERENCES ALLEN P M, ARNOLD J G, JAKUBOWSKI E, 1999. 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