Integration of remotely sensed C factor into SWAT for modelling sediment yield

HYDROLOGICAL PROCESSESHydrol. Process. 25, 3387–3398 (2011)Published online 24 March 2011 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/hyp.8066

Integration of remotely sensed C factor into SWATfor modelling sediment yield

Xianfeng Song,1* Zheng Duan,2 Yasuyuki Kono3 and Mingyu Wang1

1 College of Resources and Environment, Graduate University of Chinese Academy of Sciences, Beijing 100049, China2 Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The

Netherlands3 Center for Southeast Asian Studies, Kyoto University, Kyoto 606-8501, Japan

Abstract:

The C factor, representing the impact of plant and ground cover on soil loss, is one of the important factors of the ModifiedUniversal Soil Loss Equation (MUSLE) in the Soil and Water Assessment Tool (SWAT) to model sediment yield. The dailyupdate of C factors in SWAT was originally determined by land use types and plant growth cycles. This does not reflect thespatial variation of C values that exists within a large land use area. We present a new approach to integrate remotely sensedC factors into SWAT for highlighting the effect of detailed vegetative cover data on soil erosion and sediment yield. First, theC factor was estimated using the abundance of ground components extracted from remote sensing images. Then, the griddingdata of the C factor were aggregated to hydrological response units (HRUs), instead of to land use units of SWAT. In the end,the C factor values in HRUs were integrated into SWAT to predict sediment yield by modifying the ysed subroutine. Thissubstitution work not only increases the spatial variation of the C factor in SWAT, but also makes it possible to utilize othersources of C databases rather than those from the United States. The demonstration in the Dage basin shows that the modifiedSWAT produces reasonable results in water flow simulation and sediment yield prediction using remotely sensed C values. TheNash–Sutcliffe efficiency coefficient (ENS) and R2 for surface runoff range from 0Ð69 to 0Ð77 and 0Ð73 to 0Ð87, respectively.The coefficients ENS and R2 for sediment yield were generally above 0Ð70 and 0Ð60, respectively. The soil erosion risk mapbased on sediment yield prediction at the HRU level illustrates instructive details on spatial distribution of soil loss. Copyright 2011 John Wiley & Sons, Ltd.

KEY WORDS C factor; remote sensing; sediment yield; SWAT; Dage basin

Received 9 October 2010; Accepted 11 February 2011

INTRODUCTION

Soil erosion and sediment yield in Soil and WaterAssessment Tool (SWAT) are modelled using a ModifiedUniversal Soil Loss Equation (MUSLE) (Williams andBerndt, 1977). The vegetation cover plays an importantrole in reducing soil erosion by cutting down the force offalling raindrops, increasing infiltration of water into thesoil, reducing the speed of surface runoff and improvingphysical, chemical and biological properties of soils(Meusburger et al., 2010). Therefore, it is consideredas a crucial factor and referred to as the cover andmanagement factor (C factor) in MUSLE.

In SWAT, the C factor is a function of abovegroundbiomass, residue on the soil surface and the minimumC factor for the plant. SWAT assigns to a land usearea a calculated C factor, based on the minimum Cfactor of the corresponding plant in the ‘crop.dat’ file,its biomass and residue value at current growth stage.This is an effective method, but there are three potentialproblems. First, the C factor database included in SWATconsists of empirical values from long-term experiments

* Correspondence to: Xianfeng Song, College of Resources and Envi-ronment, Graduate University of Chinese Academy of Sciences, Beijing100049, China. E-mail: [email protected]

in the United States, which may not be applicable toother countries due to differences in vegetation systemsand management practices. Second, the estimation of theminimum C factor in SWAT may be problematic and thedefault database of the minimum C factor is subject tothe practical experiences of model developers. Third, thespatial assignment of C factors is conducted at land uselevel, which results in constant C values within a largeland use area and an inability to reflect the effect of spatialvariation in vegetation on soil loss.

Remote sensing provides a quick and easy way forestimating land surface parameters in a broad area andhas been used as an alternative approach to estimatethe C factor (de Jong et al., 1999; Ma et al., 2003).The establishment of vegetation indices such as thenormalized difference vegetation index (NDVI) has beenthe most commonly used method to determine the Cfactor (Symeonakis and Drake, 2004; Lin et al., 2002,2006). In this method, the NDVI is, in first instance,derived from remote sensing images and the C factor isthen mapped by relating the NDVI directly to C factorsamples by regression analysis. Due to the sensitivity ofthe NDVI to vitality of vegetation (de Jong, 1994), anew approach based on linear spectral mixture analysis(LSMA) of Landsat ETM data was proposed to mapthe C factor (de Asis and Omasa, 2007; de Asis et al.,

Copyright 2011 John Wiley & Sons, Ltd.

3388 X. SONG ET AL.

2008). They found that the C factor value could bedetermined as a function of fractional abundance of baresoil and ground cover (vegetation and non-photosyntheticmaterial) derived from LSMA. Compared to the C factorderived from the NDVI, LSMA produces more spatialdetails when used as an input to the Revised USLEmodel. In our previous study, we also validated thismethod in the Chaohe watershed (Duan and Song, 2008;Song et al., 2009).

In this study, we highlighted the integration ofremotely sensed C factor into SWAT to overcome thosedrawbacks that are present in the assignment of the Cfactor in SWAT. The minimum C factor for initializingthe equations of calculating daily C factors was estimatedusing remote sensing, which was then incorporated intoSWAT by modifying the ysed subroutine. The ysed inSWAT predicts daily soil loss caused by water erosionusing the modified universal soil loss equation, in whichthe minimum value of the USLE C factor for the landcover is a mandatory incoming variable. The modifiedSWAT was calibrated and validated using data collectedin the Dage basin of the upper reaches of the Chaoheriver, Hebei Province, China.

MATERIALS AND METHODS

Study area

The study basin is located in Hebei Province innortheastern China, between 41°020 –41°380N latitude and116°080 –116°450E longitude (Figure 1), a total area ofabout 1850 km2, which is upstream of the Chaohe rivercontributing to the Miyun reservoir, supplying Beijingcity with safe drinking water. The elevation of the basinvaries from 615 to 2188 m, with a mean of 1122 m. Theaverage slope is about 13°. The basin is characterized bya temperate semi-arid climate with four distinct seasons.Mean annual temperature is about 7 °C, with a meanminimum of �12 °C and a mean maximum of 24 °C.The annual precipitation is about 500 mm and the rainfalloccurs mainly during strong storms between June andAugust. Forest and shrub areas account for 44Ð1 and27Ð4% of the basin, respectively. Agricultural activitiesdominate the basin, covering 25Ð6% of its area. Thecropping system is one crop per year and the cultivationconsists largely of maize and soy beans.

DATA SETS

SWAT requires several basic databases to model the yieldof runoff and sediment. The data sources used in thiswork are described as below.

The 1 : 500 000 soil maps produced in 1990 by the SoilSurvey Office of Hebei Province and the original data ofsoil properties from the textbook in the Soil Series ofHebei Province were used. By combining the knowledgeof soil–landscape relationships with geographic informa-tion systems under fuzzy logic, the detailed soil spatial

Figure 1. Location of weather station, rain gauge stations and subbasinsin Dage Basin

data with a spatial resolution of 30 m were generatedusing the Soil Land Inference Model (Zhu et al., 2001).The main soil types are brown soil and cinnamon soil,covering 62Ð4 and 32Ð7% of total area, respectively.The brown soil dominates hilly forest area, while thecinnamon soil is distributed widely at valley farmingarea. Depending on organic matter in soil, the cinna-mon soil generally has its soil erodibility slightly higherthan brown soil. The soil texture data, originally in theKachinsky System, were first converted to the Ameri-can System and soil hydrological parameters were thenestimated with the equations proposed by Saxton et al.(1986) and Saxton and Rawls (2006).

The land use map with a scale of 1 : 100 000, producedin 1985 by the Data Center for Resources and Environ-mental Sciences, Chinese Academy of Sciences, was usedas the input of modelling crop growth in SWAT.

The 1985–1990 daily precipitation data at nine pre-cipitation observation stations were extracted from theHydrologic Yearbook, Ministry of Water Resource, Chinaand the 1960–2000 meteorological data at the weatherstation of Fengning County were obtained from the ChinaMeteorological Administration.

The 1985–1990 daily runoff and sediment yield data atthe outlet of the Dage basin (Dage hydrological station)were obtained from the Hydrological Yearbook, Ministryof Water Resource, China and used to produce monthlyrunoff and sediment yields as the product of monthlyaverage discharge and concentration (Xu et al., 2009).

The ASTER DEM with a spatial resolution of 30 mwas used for topological analysis and watershed delin-eation. On the basis of the DEM, the Dage basin wasdelineated into 25 sub-basins, which were further dividedinto 558 hydrological response units (HRUs) with aunique combination of land use type and soil type.

Estimation of the minimum C factor in SWATusing remote sensing

The calculation of the C factor originally consideredmany sub-factors such as plant canopy, ground residue,soil roughness, ridge height, soil biomass, soil consoli-dation and soil moisture. It is usually subject to ground

Copyright 2011 John Wiley & Sons, Ltd. Hydrol. Process. 25, 3387–3398 (2011)

INTEGRATION OF REMOTELY SENSED C FACTOR INTO SWAT 3389

surveys in a large geographic area. In this case, remotesensing was used to estimate the C factor. We used theapproach presented by de Asis and Omasa (2007) in ourwork. Remote sensing images, appropriate for the season,were first selected and then multiple ground componentswere extracted using LSMA for the estimation of the Cfactor in our study area.

Determination of temporal remote sensing images. TheC factor value varies throughout the year due to seasonalchanges of vegetation cover. During the stage of vigorousplant growth, the C factor can be very small, in theoryeven zero, due to the strong protection provided byvegetation at this stage. In contrast, the factor will be highdue to loose cover at plant senescence or harvest stage.To estimate the minimum C factor, it is essential to selectremote sensing images acquired during the vigorous plantstage in this study area.

The field survey and investigation of farmers showthat this area is a protected water resource conservationwatershed and there were not big changes in land usein recent decades. Mountainous forest quality is poorbecause of poor local natural environment and inadequatefinancial input. The agricultural activities dominate thisarea and the farming land is fragmented in small parcels,allocated to households under a specific land allocationpolicy in the early 1980s. The plant community consistsof simple, single crops such as soybeans or corn, or asoybean cover crop inter-cropped with corn. The cropfarming period is between late April and early October,that is, seeding is in late April and early May andharvesting in late September and early October. Thestage of vigorous growth of crops is between June andAugust, when trees and grasses also grow synchronously.Therefore, the C factor value that is estimated usingremote sensing images acquired between June and Augustcan be considered as the minimum C factor required inSWAT.

The months of June and August constitute the rainyseason in northern China. Weather conditions, i.e. fog,clouds and rain during this time, limit the quality ofoptical remote sensing images. The Landsat ETM datawith path 123 and row 31 acquired in 1 July 1999 wereselected to estimate the minimum C factor values due tothe limitation of proper remote sensing data. The multi-spectral bands of the image have a spatial resolution of30 m.

Calculation of abundance of ground components usingLSMA. A pixel on remote sensing image is a mixture ofdifferent ground components (also called end-members).An end-member is of pure surface material or a land-cover type, assumed to have a unique spectral signaturein terms of remote sensing. The idea behind LSMA isthat the spectrum recorded at a given pixel is a linear,proportional combination of the end-member spectra(Smith et al., 1990). On the basis of this assumption,the abundances of ground components are estimatedthrough spectral unmixing. The LSMA has proven to

be a reasonable interpretation and to have an acceptableaccuracy although the spectral mixture of end-membersis complicated and nonlinear (Johnson et al., 1983;Xiao and Moody, 2005). The LSMA equation and itsconstraints are as follows:

Gi Dn∑

jD1

�rijFj� C εi �1�

n∑jD1

Fj D 1, 0 � Fj � 1 �2�

where Gi is the spectral reflectance of the mixed pixelin band i, rij the reflectance of the end-member j inband i, Fj the fractional abundance of the end-memberj, εi the residual error in band i and n the number ofend-members.

In our study, we excluded the thermal band of ETMdata, hence only six multiple spectral bands were used forLSMA. The IDL/ENVI version 4Ð7 was used for imageprocessing. For image rendering, pixel values are scaledto byte values ranged from 0 to 255 (also called digitalnumbers). During spectral analysis, digital numbers (DN)are converted back to planetary reflectance values ofground components with the formula provided by Landsat7 Science Data Users Handbook.

The selection of end-members is the most critical stepin the performance of LSMA. Four end-members andtheir pixel samples were identified in this work. The min-imum noise fraction (MNF) transformation (Boardmanand Kruse, 1994) was applied to their reflectance valuesto determine the inherent dimensions of the image dataand to separate noise from the data. After the MNF trans-formation, the first four MNF-transformed bands con-tained 93Ð56% of the total information, while the othertwo contained little. Hence, only the first four MNF-transformed bands were used for subsequent processingand analysis. The pixel purity index (PPI) (Boardmanet al., 1995) for each pixel was then derived from theMNF-transformed bands to find the purest pixels in theimages for end-member selection. A total of 2039 pixelswith PPI ½ 100 were selected and projected as pointsin N-dimensional scatter plots of the first four MNF-transformed bands. The four end-members were finallyrefined by interactively rotating the MNF axes and look-ing at these PPI pixels in this N-dimensional space (Gal-vao et al., 2005). Figure 2 shows the spectral curves ofthe four end-members: vegetation, bare soil, rock andwater/shadow.

After determining end-members and their spectralvalues for each band of Landsat ETM data, each fractionof the four end-members can be derived by solvingEquation (1), with Equation (2) as a constraint. Theprogram written with IDL was used to derive the fractionsof the four end-members and the root mean square error(RMSE). The results are shown in Figure 3 where thecolour white means a high fraction of an end-member ora large error within a pixel. The overall average RMSEfor the whole image is about 0Ð15.


3390 X. SONG ET AL.

Figure 2. Spectral reflectance of selected end-members purified for linearspectral unmixing

Figure 3. The abundance of end-members extracted from Landsat ETMby linear spectral unmixing

As the remote sensing images used in our studyconsist of historical data, it is difficult to verify theLSMA-derived abundances of end-members by groundtruth data. We indirectly validated the separation resultsas follows. The NDVI has been generally consideredto be a useful indicator of vegetation cover, hence acorrelation analysis between the NDVI images and the

images of vegetation fractions by LSMA was conducted.The correlation coefficient was high with a value of 0Ð91.As identified by field surveys, the fractional images alsoshow that the area, where soil erosion is relatively severedue to loose vegetation cover, is dominated by bare soil.

Estimation of the C factor using abundance of groundcomponents. The C factor was estimated followingthe method proposed by de Asis and Omasa (2007).The water/shadow fraction was first removed because ashadow is not a physical component and there is no soilerosion in water bodies. The three remaining fractions(vegetation, bare soil and rock) were rescaled by a nor-malization factor as shown in Equation (3), with the sumof the vegetation (Fveg), bare soil (Fbs) and rock (Frock)fractions equal to 1.

f D 1

1 � Fwater/shadow�3�

Then the C factor was estimated using the threerescaled fractions with the following equation:

C D f ð Fbs

1 C f ð Fveg C f ð Frock�4�

where Fbs is the bare soil fraction, Fveg the vegetationfraction and Frock the rock fraction. The C factor map isshown in Figure 4.

Zonal analysis of C factor with land use type. Ageo-statistics analysis was conducted by overlaying theestimated C factor map (Figure 4) with a land use mapusing zonal analysis of ArcGIS. The average C factorvalues for different land uses were calculated as shownin Table I. It can be seen that the average C factorvalues reflect the fractions of vegetative cover of theircorresponding land use types. That is, the less a land usetype has its vegetative cover, the larger its C value is. Forexample, the C factor values of forest lands (wood lands,

Figure 4. C factor map estimated using the abundance of end-memberswith a spatial resolution of 30 m



Table I. Average C factor values of land uses

Land use type C values Land use type C values

Wood land 0Ð028 Shoaly land 0Ð620Shrubbery land 0Ð071 Sandy land 0Ð706Sparse wood land 0Ð073 Residential quarters 0Ð449Orchards 0Ð213 Exposed rock and shingle land 0Ð047High coverage grassland 0Ð132 Mountainous dryland 0Ð312Medium coverage grassland 0Ð215 Hilly dryland 0Ð325Low coverage grassland 0Ð533 Plain dryland 0Ð224

shrubbery and sparse wood lands) increase when theirfractions of canopy cover decrease, given the definitionof the National Standard for Land Use Classification.The same characteristics occur within grasslands (highcoverage grassland, medium coverage grassland and lowcoverage grassland).

This analysis of interrelations illustrates that the Cfactor values, estimated in this study, are relatively con-sistent internally, suggesting that their range is reason-able, although we cannot determine the accuracy of theirabsolute values at a pixel level. Nevertheless, it is stillessential and practical to produce such an estimated Cfactor map for initializing SWAT, as our main purposewas to identify crucial erosion areas in this basin foreffective protection measures.

Integration of the minimum C factor determinedby remote sensing into SWAT

Among the physical processes supported by SWAT,the hydrological cycle is the driving force behind otherprocesses that occur in the watershed. In SWAT, awatershed is partitioned into a number of HRUs, allowingspatial reference to different areas of the watershed. Theinputs and outputs of SWAT are grouped and organizedaccordingly. However, the C factor derived from remotesensing data is pixel -based. To integrate remotely sensedC factor data into SWAT, the following three stepsare required: identification of the spatial location ofHRUs, aggregation of remotely sensed C factor data inHRUs and modification of the original source code forassignment of the C factor.

Identification of spatial location of HRUs. TheAVSWATX tool of SWAT does not explicitly generate aspatial layer of HRUs using an overlay function. Instead,it partitions HRUs using a specific area-weighted method,thus producing an ASCII file referred to as ‘HruLandus-eSoilRepSwat.txt’ to store all the information on HRUs.In this file, each HRU is identified by the sub-basin ID,land use type and soil type to which it belongs, but itsspatial coverage is not defined.

The same spatial data sets such as land use map, soilmap and sub-basin map using ArcGIS 9Ð2 were overlain,generating the spatial distribution of all HRUs. On thebasis of the multicolumn indices (sub-basin ID, land usetype and soil type), each HRU was assigned a spatiallocation or coverage area. Figure 5 shows the spatial

Figure 5. Spatial distribution of 558 HRUs in Dage Basin

distribution of HRUs. There are 558 colours in all, eachrepresenting one HRU.

Aggregation of remotely sensed C factor data in HRUs.The C factor value for each HRU was determined bydividing the sum of the C factor values for all pixelsin an HRU by the number of pixels in the HRU. Thisprocess was conducted using the Zonalmean function inARCGIS 9Ð2 with the remotely sensed C factor map as avalue grid input and spatial location map of HRUs as azonal grid input. The mean C factor value for each HRUwas generated, as shown in Figure 6.

An auxiliary file named ‘hruc.txt’ was generated simul-taneously. There were two fields in the file, HRU ID andUSLE C, which represent the ID for each HRU and theircorresponding minimum C factor values. There were 558records in total.

Modification of original source code for loading a newC factor. The assignment of a C factor to an HRUin SWAT was originally determined by assigning thecorresponding USLE C value in ‘crop.dat’ according tothe land use/land cover to which each HRU belongs. Thisproduces a homogenous C factor map, with little spatialvariation within a specific land use area.

To make use of remote sensing C factor data, thesource code in the ysed subroutine (ysed.f) for dealingwith the C factor was modified to assign the correspond-ing USLE C in the ‘hruc.txt’ file for each HRU according


3392 X. SONG ET AL.

Figure 6. C factor values of 558 HRUs

to their HRU ID. Improved source codes were compiledwith other original source codes and a new executablefile SWAT2005.exe was generated for further runoff andsediment simulation.

Modified SWAT calibration and validation

Evaluation criteria of model performance. Two mostused statistical indicators, Nash and Sutcliffe’s efficiencycoefficient (ENS) (Nash and Sutcliffe, 1970) and thecoefficient of determination (R2) were used to evaluatethe performance of the model. These indices are obtainedfrom Equations (5) and (6).

ENS D 1 �∑n

iD1�Oi � Pi�

2

∑n

iD1�Oi � O�2

, �5�

R2 D(∑n

iD1�Oi � O��Pi � P�

)2

∑n

iD1�Oi � O�2

∑n

iD1�Pi � P�2

�6�

where n is the number of observations, Oi and Pi theobserved and predicted data at time i, O and P the meanof observed and predicted data. The closer the valuesof ENS and R2 are to 1, the better the model performs.The model performance is considered to be acceptable orsatisfactory when the ENS value and R2 are greater than0Ð5 and 0Ð6, respectively (Santhi et al., 2001; Kang et al.,2006).

Sensitivity analysis. There are a great many parametersin the SWAT model due to the fact that it takes spatialheterogeneity into consideration. A large number of theseparameters that cannot be obtained directly from availabledata can be estimated through a calibration process.Referring to the SWAT user manual (Neitsch et al., 2002)and the publications by Santhi et al. (2001), Kannanet al. (2007), Xu et al. (2009) and Duan et al. (2009),fourteen sensitive parameters, eight for runoff and sixfor sediment yield, were selected to reduce the number of

parameters to be calibrated and to improve the efficiencyof the calibration. The definitions of these parameters,their ranges and default values are shown in Table II.

Calibration and validation. Simulation was carried outfor every month from 1985 to 1990 using measuredtotal stream flow, separate surface runoff, base flow andsediment yield data at the Dage station. The data from1985 to 1987 were used for calibration and the data from1988 to 1990 for validation. The calibration procedurechart proposed by Santhi et al. (2001) was followed. Thecalibration mainly involves the adjustment of parametersto make the simulated data match the measured data. Inthis study, a trial-and-error method was used to adjustparameters within their ranges.

Runoff was calibrated and validated first due to itseffect on sediment yield predictions. In the runoff cal-ibration, surface runoff and base flow data were doneseparately by comparing the simulated with the separateddata from the total stream flow. First, surface runoff wascalibrated until monthly ENS > 0Ð50 and R2 > 0Ð6. Thesame criteria were applied to base flow, while surfacerunoff was continually rechecked because the base flowcalibration parameters also affect surface runoff (Santhiet al., 2001). After runoff calibration, the sediment yieldwas calibrated and this process was stopped when thevalues of ENS and R2 met the criteria mentioned earlier.The final calibrated values for the fourteen parametersare shown in Table II.

Model validation is the process of performing simu-lation with a different data set from the calibration dataset, keeping the calibrated parameters unchanged. Thevalidation data set is used to test whether the calibratedparameters were appropriate for the study basin. Themeasured total stream flow and sediment yield and theseparated base flow and runoff at the outlet of the studyarea (Dage station) from 1988 to 1990 were used to vali-date the model. The same statistical indicators were usedto evaluate the model performance.

RESULTS AND DISCUSSION

Base flow separation

An accurate stream flow simulation is the basis andpremise for a subsequent sediment simulation. The streamflow measured at the outlet of a watershed is composedof a surface runoff and a base flow. The surface runoffis the main driving force of soil erosion while the baseflow is not. Thus, it is essential to separate the baseflow from the steam flow for further sediment simulation.The agreement between observed and simulated data interms of stream flow at the outlet of a watershed cannotguarantee that the fractions of water yield contributedby surface runoff and base flow are reasonable due tothe fact, all else being equal, that there is more thanone combination of parameters that may result in similarsimulation results (Beven, 1993; White and Chaubey,2005).



Table II. Calibration of SWAT parameters

Modelprocesses

Parameters Values

Name Description Ranges Default Calibrated

Runoff CN2 Initial SCS runoff curve number for moisturecondition II

20–100 36–87 C6

ALPHA BF Base flow alpha factor (days) 0–1 0Ð95 0Ð0108SOL AWC Available water capacity of the soil layer (mm

H2O/mm soil)0Ð00–1Ð00 Varies with soil types C5%

ESCO Soil evaporation compensation factor 0Ð00–1Ð00 0Ð95 0Ð30REVAPMN Threshold depth of water in the shallow aquifer

for ‘revap’ or percolation to the deep aquiferto occur (mm H2O)

0–500 0 10

SLSOIL Slope length for lateral sub-surface flow (m) 10–150 0 SLSUBBSNC10SOL K Saturated hydraulic conductivity (mm/h) 0–100 Varies with soil types �10%CH K2 Effective hydraulic conductivity in main channel

alluvium (mm/h)0–150 0 1Ð8

Sediment PRF Peak rate adjustment factor for sediment routingin the main channel

0–2 1Ð0 2

ADJ PKR Peak rate adjustment factor for sediment routingin the sub-basin (tributary channels)

0Ð5–2 1Ð0 2

SPCON Linear parameter for calculating the maximumamount of sediment re-entrained duringchannel sediment routing.

0Ð0001–0Ð01 0Ð0001 0Ð01

SPEXP Exponent parameter for calculating sedimentreentrained in channel sediment routing

1Ð0–2Ð0 1Ð0 1Ð6

USLE C Minimum USLE C factor for water erosionapplicable to the land cover/plant

0–1Ð0 varies HRUs related

USLE P USLE equation support practice factor 0Ð1–1Ð0 1Ð0 1Ð0

The operator (C) means that a calibration value equals a default value plus a given increment, while the operator (�) represents a decrement. Thepercent (%) denotes that such an increment or decrement is the percentage of the default value, not absolute one.

The base flow filter program (Arnold and Allen, 1999)was introduced to separate explicitly the base flow fromthe measured stream flow. The output of the programprovides for three passes representing the fraction ofwater yield contributed by the base flow. The first passdenotes the highest fraction, the third pass the lowest andthe second is medium fraction. As well, this programestimates a base flow recession constant (ALPHA factor)and the number of base flow days (the number of days forthe base flow recession to decline through one log cycle).These are referred to, respectively, as the ALPHA BFand GW DELAY parameters in SWAT and are usedto account for sub-surface water response simulated bySWAT (Larose et al., 2007).

Table III shows the separation results generated usingdaily stream flow data at the Dage station from 1985to 1990. The base flow fraction ranges between 59%(Baseflow Fr3) and 73% (Baseflow Fr1). According tothe base flow filter instruction, it often falls somewherebetween the value separated using the first pass andsecond pass, but Kannan et al. (2007) reported thatthe third pass provided a reasonable base flow in theirstudy watershed. The selection of an appropriate pass forseparating base flows depends on the characteristics ofthe study area of interest, i.e. the pattern of precipitation(seasonal/monsoon, annual or other periods), the variousland-cover categories in the watershed (e.g. more urbanland cover will produce more surface runoff), topography

Table III. Base flow separation by the base flow filter programof SWAT

BaseflowFr1

BaseflowFr2

BaseflowFr3

NPR ALPHAfactor

Baseflowdays

0.73 0Ð64 0Ð59 19 0Ð0108 213

Baseflow Fr1 is the fraction of stream flow contributed by base flowin first pass, Baseflow Fr2 the second pass and Baseflow Fr3 the thirdpass. NPR means the number of individual base flow recessions used tocalculate master recession curve.

(high slope areas produce more surface runoff), soils, thepresence of any tile drains, and so on.

After an analysis of the characteristics of the studyarea and a consultation with the developers of thisfilter program, the average of the separated base flows,using the first and second pass, was used in our study,representing 68Ð5% of the stream flow. Figure 7 showsthe measured monthly stream flow and separated baseflow at the Dage station. The monthly surface runoffwas obtained by subtracting the separated base flowfrom the measured stream flow. The data of surfacerunoff, base flow and stream flow were used to calibratecorresponding components in the SWAT model separatelyto guarantee stream flow simulation as well as thepartition of stream flow into surface runoff and baseflow.


3394 X. SONG ET AL.

Figure 7. Measured monthly stream flow and separated base flow at Dage station

Figure 8. Time-series plot of measured and predicted monthly surface runoff at Dage station during the calibration (1985–1987) and validationperiods (1988–1990)

Figure 9. Time-series plot of measured and predicted monthly base flow at Dage station during the calibration (1985–1987) and validation periods(1988–1990)

Runoff simulation

The time-series plots of predicted and measuredmonthly surface runoff, base flow and stream flow at theDage station during the calibration and validation phasesof the model assessment are shown in Figures 8–10,respectively. The predicted flows during both the cali-bration and validation periods basically matched the mea-sured flows, but underestimated stream flows during thespring and winter and overestimated stream flows duringthe summer and fall.

The underestimation of stream flows during the springand winter months could be attributed to the fact thatthe stream flow in the spring and winter mainly orig-inates from snowmelt in the Dage basin and SWATgenerally underestimates snowmelt-driven flows (Bena-man et al., 2005; Srinivasan et al., 2005). Fontaine et al.(2002) concluded that a band elevation for simulat-ing snowmelt was needed to represent the relation-ship between atmospheric temperature and elevation(Gassman et al., 2007). However, the fact that there is



Figure 10. Time-series plot of measured and predicted monthly stream flow at Dage station during the calibration (1985–1987) and validation periods(1988–1990)

only one meteorological station is at this area and notenough data for creating the band elevation limited itsapplication in our study.

The overestimation of stream flows during the summerand fall months was also reported for the same watershedby Xu et al. (2009). They attributed the overestimationto the lack of a warm-up period in the processesof SWAT simulation, because of the short time-seriesof the observations. In addition, the overestimation offlows during the summer and fall could be due tothe underestimation of evapotranspiration, which wasobtained using the weather generator in SWAT, withthe data of only one meteorological station as input(Benaman et al., 2005).

It should also be noted that there were numerous uncer-tainties and limitations which affected the simulationresults. For instance, the uncertainty existing in the soiltexture conversion from the Kachinsky System to theAmerican System could lead to error in soil propertiesand further resulting in the error estimation of soil watercharacteristics that significantly affect sub-surface com-ponents such as infiltration and base flow. In addition, thespatial representation of climate data has a great impacton the accuracy of model simulation in large areas, espe-cially in mountains (Hernandez et al., 2000; Moon et al.,2004).

Table IV. Evaluation of hydrological process simulation

Simulationphases

Surfacerunoff

Baseflow

Total streamflow

ENS R2 ENS R2 ENS R2

Calibration(1985–1987)

0Ð69 0Ð73 0Ð60 0Ð69 0Ð74 0Ð79

Validation(1988–1990)

0Ð77 0Ð87 0Ð70 0Ð73 0Ð75 0Ð84

The quality of flow simulation is evaluated by acomparison of the predicted and measured flows. FromTable IV, it can be seen that ENS varies from 0Ð60 to0Ð74 and R2 from 0Ð69 to 0Ð79 for all flows duringthe calibration and validation phases. The coefficientsENS and R2 for surface runoff, base flow and totalstream flow are greater than 0Ð5 and 0Ð6, respectively.This indicates that SWAT could satisfactorily simulatehydrological processes and the simulated fractions ofsurface runoff and base flow are suitable for furthersediment simulation.

Sediment simulation

The time-series plots of monthly predicted and mea-sured sediment yields at the Dage station during the cali-bration and validation phases is shown in Figure 11. The

Figure 11. Time-series plot of measured and predicted monthly sediment yields at Dage station during the calibration (1985–1987) and validationperiods (1988–1990)


3396 X. SONG ET AL.

Table V. Evaluation of sediment yield simulation

Simulation phases ENS R2

Calibration (1985–1987) 0Ð66 0Ð70Validation (1988–1990) 0Ð63 0Ð89

trends of predicted sediment yields matched the measuredsediment data fairly well. The overestimation and under-estimation of sediment were similar to their correspond-ing surface runoff, as surface runoff is the most importantfactor for computing sediment yields in MUSLE.

Figure 11 shows a large discrepancy between thepredicted and measured sediment yields in some years.This could be caused by some inherent deficiency ofSWAT. Benaman et al. (2005) reported that SWAT wasunable to capture extreme flood events with a sedimentload of almost 60 000 metric tonnes. In our study,we found that the flood sediment load in some yearscould be very high. For example, it reached 539 000metric tonnes on 7 July 1986. There are many othersources of uncertainties, i.e. errors in measured data orthe limitations of the model for using simplistic equationsto calculate sediment yield and sediment transport.

Table V lists the evaluation data for sediment yieldsimulation. The ENS and R2 values were 0Ð66 and 0Ð70during the calibration phase and 0Ð63 and 0Ð89 duringthe validation phase, respectively. These values, abovethe satisfactory criteria levels, indicate that the modelperformed well for sediment yield prediction. SWATestimated the accumulated sediment loadings over 3- to6-year periods within 5 and 15% of the measured dataduring both the calibration and validation phases of themodel, respectively. To some extent, this mitigates someof the uncertainties, showing a good fit between predictedand measured data.

Identification of critical erosion areas

SWAT computes sediment yields for each HRU or sub-basin and saves its results in the files of ‘output.hru’

Table VI. Statistical data of soil erosion intensity class and arearatios

Soil erosion risk Erosionmodulus

(tÐha�1Ðy�1)

Area (%)at sub-basin

level

Area (%)at HRU

level

Slight <5 10Ð00 56Ð40Light 5–25 29Ð52 17Ð09Moderate 25–50 43Ð10 7Ð89Strong 50–80 15Ð97 6Ð69Extremely strong 80–150 1Ð41 4Ð89Severe >150 — 7Ð04

and ‘output.sub’, respectively. Divided by the area of acomputation unit and its related soil delivery ratio, we canobtain the soil erosion intensity of any HRU or sub-basin.Following the soil erosion risk classification specificationendorsed by the Ministry of Water Resources of China(1997), the soil erosion risk maps (Figure 12) at both theHRU and sub-basin levels were produced.

More erosion classes and spatial details at the HRUlevel were revealed than at the sub-basin level. This isclearly illustrated on the HRU map, showing the strong,extremely strong and severe erosion areas distributedalong the river, which is very instructive for users. Thoseareas near rivers are dominated by agricultural land,residential areas, sandy lands and other uses.

Table VI presents the statistical data of soil erosionrisk classes. The area of soil erosion risk classes is sig-nificantly different at the sub-basin and HRU levels. Sim-ulation at the HRU level provides a more reasonableresult in this water resource conservation area accord-ing to field surveys and historical data. The sedimentsimulation results at the HRU level reflect spatial vari-ation and crucial areas can be precisely identified. Atthis point, a C factor map produced at the HRU levelcould enhance such spatial variation of sediment simula-tion results, rather than the variation generated by defaultat land use level, because an HRU is an intersection resultfrom the overlap of a sub-basin map, a soil map and aland use map.

Figure 12. Soil erosion risk maps at subbasin and HRU levels



CONCLUSIONS

This study estimated the minimum C factor in SWATusing remote sensing data acquired at the stage ofvigorous plant growth and integrated it into SWAT atthe HRU level, not the land use level. In comparisonwith the C factor originally determined in SWAT by landuse type and the empirical values from the United States,this method increases the spatial variation of the C factorvalues within the same land use, particular in a large area,enabling it to reflect the real scenarios of heterogeneousvegetation cover in a particular geographical area.

The C factor, obtained by remote sensing, was suc-cessfully integrated into SWAT to predict sediment yieldby following three steps. First, the spatial distribution ofHRUs was identified, then the gridding data of the C fac-tor was aggregated to those HRUs and finally the ysedsubroutine of SWAT was modified to load this aggrega-tion C factor file for the estimation of sediment yield. Thedemonstration in the Dage basin shows that the use of aninitial minimum C factor data set produced using remotesensing in SWAT can result in a good fit between waterflow and sediment yield predicted by SWAT and thosemeasured at the outlet of a watershed. The soil erosionrisk map produced using sediment yield prediction dataat the HRU level also shows an enhanced ability in cap-turing and retaining spatial details on spatial distributionof soil loss in Dage basin.

ACKNOWLEDGEMENTS

This work was partly supported by the National BasicResearch Program (973 Program, Nos. 2010CB428801and 2010CB428804) of China, the National Natural Sci-ence Foundation of China (No. 40771167) and the 2007Grant-in-Aid Program of Kurita Water and EnvironmentFoundation of Japan. We would like to thank Jeff Arnoldand Narayanan Kannan for their help and suggestionin baseflow separation. We are also indebted to twoanonymous reviewers whose comments notably helpedus improve the manuscript.

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Integration of remotely sensed C factor into SWAT for modelling sediment yield

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