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Agricultural Water Management 133 (2014) 65– 73

Contents lists available at ScienceDirect

Agricultural Water Management

j ourna l h omepage: www.elsev ier .com/ locate /agwat

ow land use change contributes to reducing soil erosionn the Jialing River Basin, China

hanghong Zhang ∗, Yan Liu, Taiwei Wangenewable Energy School, North China Electric Power University, Beijing 102206, China

r t i c l e i n f o

rticle history:eceived 11 April 2013ccepted 31 October 2013vailable online 1 December 2013

eywords:ialing River Basinand useediment yieldlope

a b s t r a c t

A lack of adequate land use planning can contribute to severe soil erosion and increased sediment yield,creating major environmental problems and threatening the sustainable development of economies andsocieties. The objectives of this study were to model the effects of land use change on reducing soil erosionin the Lizixi watershed, a typical agricultural area in the Jialing River Basin, China. The Soil and WaterAssessment Tool (SWAT) was used to model daily runoff and sediment data from this basin collected from1975 to 1995. First, the model was used to determine acceptable values of the coefficient of correlation(R2) and the Nash–Sutcliffe coefficient (Ens) during both the calibration and validation periods. Next, themodel was used to determine which kind of crop can be grown with the least sediment yield and soilerosion. The results indicated that wheat fields caused the least soil erosion, followed by sweet potatoes,

WAT corn, and rape, the last of which was the least effective in preventing soil erosion. The entire watershedwas divided into four regions with various slopes, categorized into areas with slopes of 0–5◦, 5–15◦,15–25◦ and >25◦ to determine how slope influences soil erosion. The results of slope analysis showed soilerosion was the most severe in areas with slopes >25◦. We recommend areas with slopes >25◦ shouldbe used as forestland or grassland rather than farmland. This study will assist local governments inimplementing sustainable soil conservation actions designed to reduce soil erosion.

. Introduction

Soil erosion, a major global environmental problem, can resultn negative impacts that limit the sustainable development ofconomies and societies. Soil erosion and inadequate watershedanagement cause the loss of fertile soil, result in serious silt-

ng in streams, rivers and reservoirs. They also reduce the watertorage capacity of surface soil and increase the frequency andntensity of floods and droughts. Heavy metals and other non-pointollutants, chemical fertilizers and pesticides can also be trans-orted with sediments by attaching themselves to the soil particlesSteegen et al., 2001; Yi et al., 2011). The Jialing River is one ofhe most important tributaries of the Yangtze River in China andows through Shanxi, Gansu and Sichuan provinces and the cityf Chongqing. Its watershed covers an area of 159,800 km2. Thisiver carries more sediment than any river in Sichuan Province andoil erosion impacts its watershed, the second largest watershedf any river system in the upper Yangtze River basin. A combi-

ation of topographical and geological features creates these high

evels of soil erosion. Loess soil is easily eroded in the upstreamrea; fuchsia shale is also easily eroded by air-slake in the middle

∗ Corresponding author. Tel.: +86 10 61772405; fax: +86 10 61772234.E-mail address: zhangsh928@126.com (S. Zhang).

378-3774/$ – see front matter. Crown Copyright © 2013 Published by Elsevier B.V. All rittp://dx.doi.org/10.1016/j.agwat.2013.10.016

Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved.

stream and downstream regions. These conditions combined withsteeply sloped stream and river banks, excessive cultivation and theresulting poor vegetative coverage to cause widespread soil ero-sion throughout the entire Jialing River Basin. Soil erosion mainlyoccurs on sloping cropland, which accounts for 60–80% of the soilerosion in the Jialing River Basin. An initial analysis of field datashowed (1) the intensity of soil erosion occurring on sloping crop-land is 10–20 times higher than that of grassland and woodland, (2)the annual mean soil erosion with slopes between 15◦ and 25◦ is5000–6000 t/km2, and (3) slopes >25◦ are generally heavily or veryheavily eroded (Huang et al., 2002). Identifying critical areas whichare most likely to experience erosion in the region is importantbecause the ecological and physical characteristics of the JialingRiver Basin affect the local water resources and environment, butalso affect water quality of the Yangtze River (Wu et al., 2012; Yiet al., 2010). Knowing how agricultural activities impact soil ero-sion will also assist local governments in the design and efficientapplication of soil and water conservation measures.

Several factors contribute to soil erosion, such as hilly topogra-phy, high intensity rainfall, soil types, poor agricultural practicesand current land use conditions (Irvema et al., 2007; Hessel and

Jetten, 2007). Among them, land use is a key factor affecting thelevel of soil erosion since changes in land use can change the char-acteristics of runoff and alter the climatic conditions in a region(Hernández et al., 2005; Casalí et al., 2010; Wei et al., 2010). Slope

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s another important factor affecting the soil erosion level in aatershed (Liu et al., 2013; Cui et al., 2012). Shen et al. (2010)

nalyzed the soil conservation measures in the Three Gorges Reser-oir Area in China by constructing experimental plots and foundhat the soil erosion rate was different when planting tea trees,eanuts and orange trees. Kateb et al. (2013) studied the soil erosionnd surface runoff on different vegetation covers and slope gradi-nts; the results showed that horticulture and agriculture landst slope < 30◦ had no difference in the soil loss; tea plantations atlope> 30◦ proved to be the most susceptible vegetation cover torosion; the rate of erosion is substantially affected by changes inegetation cover. In recent years, many scholars have studied theactors influencing soil erosion and hydrology in the Jialing Riverasin using remote sensing and related data (Wang and Yao, 2005;u et al., 2008; Gui and Jiao, 2010). Zhang et al. (2003) used atatistical prediction model, a back-propagation network model,nd a double mass-curve prediction model to predict the trendsn runoff and sediment in Jialing River Basin. Ding et al. (2008)pplied a mathematical model in the Jialing River Basin to revealhe causes of changes in annual runoff and sediment transport, andlso studied the relationships between precipitation and sedimentransportation as well as between human activities and sedimentransportation. All these research studies illustrated the impacts ofand use change on sediment yield and soil erosion. The influencesf a single land use type and the influence of a range of slopes onediment yield and soil erosion are of great importance for this typef research. Quantifying the effects of different land use scenariosn the spatiotemporal changes in sediment yield is crucial to theevelopment of water resources and land use management in the

ialing River Basin.The objectives of this study were (a) to evaluate the impacts

f different types of land use change on soil erosion and sedimentield, and (b) to quantify the effects of slope on sediment yield.

Hydrological models are becoming more popular as tools thatllow researchers to describe the complicated processes of theater cycle. These models have been widely used in planning for

he development of water resources and flood control, for urbanlanning and the evaluation of non-point pollution and relatedesearch on the responses of watersheds to human activity (Liut al., 2006; Jiang et al., 2012). With the rapid development of GISnd RS technology, physically based distributed hydrological mod-ls have the advantage of reflecting the spatial characteristics ofhange in a watershed, so they are receiving more and more atten-ion (Shrestha et al., 2007; Loague et al., 2006). Several distributedydrological models such as the Universal Soil Loss EquationUSLE), the Water Erosion Prediction Project Model and the Soilnd Water Assessment Tool (SWAT) have been used to study thempacts of land use change and soil conservation measures on soilrosion and sediment yield (Beskow et al., 2009; Mengistu et al.,012; Antje and Martin, 2009). In this study, the SWAT model waspplied to assess the contribution of land use change to reducingoil erosion and sediment yield. The SWAT model is semi-physicallyased and allows simulations using detailed spatial data by divid-

ng the watershed into a large number of sub-watersheds. The maindvantages of using the SWAT model are that it allows researcherso simultaneously consider hydrological, environmental, biologicalrocesses as well as land management techniques based on avail-ble databases; the SWAT model is very flexible and user friendlyDouglas-Mankin et al., 2010; Jayakrishnan et al., 2005; Wu andohnston, 2007; Lam et al., 2010; Parajuli et al., 2008).

. Study area

The Lizixi watershed covers 697.5 km2 and lies between05◦47′–106◦06′ E and 30◦22′–30◦42′ N in the downstream portion

anagement 133 (2014) 65– 73

of the Jialing River Basin (Fig. 1). This region’s subtropical humidmonsoon climate experiences wide variation in its annual and sea-sonal rainfall patterns. Typically, 650–1000 mm of precipitationfalls annually, mostly from May to October. The annual mean flowis 4.02 m3/s, the maximum flow is 525 m3/s and the minimum is0. The main land use types in the watershed are hill drylands, hillpaddy fields and forest. The main soil types are purple soil, neutralpurple soil and paddy soil.

The dry land area to paddy field ratio in Lizixi watershed is about3:1 and their combined area accounted for >80% of the entire water-shed area. The irrigated area of the Lizixi watershed is 109.1 km2.The watershed is well-suited to growing wide variety agriculturalproducts on very rich soils which receive abundant rainfall. Thedominant food crops grown in both the Lizixi watershed as wellas in the expansive Sichuan hilly region include rice, rape, wheat,corn and sweet potatoes. The geography, farming systems, rainfallpatterns and local community conditions in the Lizixi watershedare typical of the Jialing River Basin. The study of the characteris-tics of soil erosion in this watershed will assist land managers indeveloping policies designed to manage soil erosion.

3. Materials and methods

3.1. Data collection

Digital Elevation Model (DEM), land use, soil and weather datawere combined for this analysis (Table 1). First, DEM data were usedto delineate the Lizixi watershed into 35 sub-watersheds. Second,land use data were classified into five land use types including hilldrylands, hill paddy fields, forests, grasslands and urban areas cov-ering 72.94%, 15.95%, 10.98%, 0.09% and 0.04% of the watershed,respectively. Third, soil data were combined with the spatial soildistribution map and physical attribute data; the resulting mapdisplayed four soil types, purple, neutral purple, paddy and allu-vial soils which covered 43.95%, 29.09%, 26.83% and 0.13% of theLizixi watershed, respectively. The most important data on physi-cal soil attributes were the soil particle data; some soil parameterswere derived from it, such as the saturated hydraulic conductivity,moist bulk density and available water capacity of the soil layer.Because the SWAT model used the same classification standard forsoil particles as the mapped soil data, the physical attribute datawere applied directly to the SWAT model with no conversion. Next,daily precipitation data as well as maximum and minimum air tem-perature, wind speed and humidity data were acquired. Finally,daily runoff and sediment yield data were used to calibrate andvalidate the SWAT model. Sediment yield data for October–Maywere missing because of the small amount of rainfall and runoffreceived during this time making it difficult to measure the sedi-ment concentration.

3.2. The SWAT model

The SWAT model is a continuous, watershed-scale, physicallybased distributed hydrological model developed by the UnitedStates Department of Agriculture-Agricultural Research Service(Neitsch et al., 2002). It can predict the impact of land manage-ment practices on the hydrologic cycle, on sediment transport andon agricultural pollutants occurring in different soil types, land usetypes and management conditions (Arnold et al., 1998; Arnold and

Fohrer, 2005; Behera and Panda, 2006). The SWAT model dividesthe watershed into sub-watersheds and then the sub-watershedsare divided into hydrological response units (HRUs) with uniquecombination of land use, soil and slope types.

S. Zhang et al. / Agricultural Water Management 133 (2014) 65– 73 67

atersh

t

S

wsopWsfl

Ufwptse

s

TA

Fig. 1. The location of Lizixi w

The hydrologic cycle is simulated by the SWAT model based onhe water balance equation:

Wt = SW0 +t∑

i=1

(Rday − Qsurf − Ea − Wseep − Qgw)i

(1)

here STt is the final soil water content (mm), SW0 is the initialoil water content (mm), t is the time (days), Rday is the amountf precipitation per day (mm), Qsurf is the amount of surface runoffer day (mm), Ea is the amount of evapotranspiration per day (mm),seep is the amount of percolation and bypass flow exiting in the

oil profile bottom per day (mm), and Qgw is the amount of returnow/baseflow per day (mm).

Soil erosion in the SWAT model is calculated using a Modifiedniversal Soil Loss Equation (MUSLE). The USLE model uses rain-

all as the erosion energy factor and the MUSLE model replaces itith runoff (Neitsch et al., 2005). This replacement can improve therediction accuracy, reduce the requirement of using a sedimentransport ratio and allows the estimation of the sediment yield of aingle rainstorm. Sediment yield is calculated using the following

quation:

ed = 11.8(Qsurf × qpeak × areahru)0.56 × KUSLE × CUSLE × PUSLE

× LSUSLE × CFRG (2)

able 1 brief description of the SWAT input data and measured runoff and sediment yield data.

Data type Time (year) Resolution

DEM 1998 30 m resolut

Land use 1986 1:100,000

Soil data 1986 1:1,000,000

Precipitation 1975–1995 Daily

Maximum and minimum airtemperature, wind speed andhumidity

1975–1998 Daily

Runoff and sediment yield 1975–1995 Daily

ed, Jialing River Basin, China.

where sed is the sediment yield (t), Qsurf is the surface runoff volume(mm), qpeak is the peak runoff rate (m3/s), areahru is the area of aHRU (km2), KUSLE is the soil erosion factor, CUSLE is the land coverand management factor, PUSLE is the conservation measures factor,LSUSLE is the topographic factor, and CFRG is the coarse fragmentfactor.

3.3. Methodology

3.3.1. Hydrological parameters extraction based on DEMHydrological parameters need to be extracted prior to conduct-

ing hydrological simulation. This mainly involves river networkgeneration, sub-basin delineation and the partitioning of hydro-logical response units.

The SWAT model uses an automatic basin separating subrou-tine which applies TORAZ software (Garbrecht and Martz, 1995) toconduct terrain analysis to automatically generate a digital rivernetwork, which is then divided into sub-basins based on the rivernetwork data. When delineating and classifying the river network,an adjustable minimum river watershed area threshold should

be determined first. The smaller the threshold value is, the moredetailed and dense the generated river network will be. Conversely,the greater the threshold value is, the rougher and more looselyorganized the network will be. It is important, however, to note

Source

ion International Scientific Data Service Platform(http://datamirror.csdb.cn/admin/datademMain.jsp)Data Sharing Infrastructure of Earth System Science(www.geodata.cn)

Environment and Ecological Science Data Center for WestChina (http://westdc.westgis.ac.cn/data)Anfu, Zhaojiaci, Lianghekou and Taiping gauging stationsAnfu Gauging Station

Anfu Gauging Station

68 S. Zhang et al. / Agricultural Water Management 133 (2014) 65– 73

Table 2Sensitive parameters for runoff simulation and calibrated values in SWAT.

The most sensitive parameters Parameter definition Range Ranks of the parameters Calibrated value

CN2 Initial SCS CN II value ±20a 1 −2%a

SOL AWC (mm) Soil available water capacity ±30%a 2 +15%a

ALPHA BF (days) Base flow alpha factor ±30%a 3 −20%a

ESCO Soil evaporation compensation factor 0.01–1 4 0.32SLSUBBSN (m) Average slope length 10–150 5 15–110 (varies by subbasins)CH K2 (mm/h) Channel effective hydraulic conductivity 0–100 6 22

a The percentage by which the original values changed.

Table 3Sensitive parameters for sediment yield simulation and calibrated values in SWAT.

The most sensitive parameters Parameter definition Range Ranks of the parameters Calibrated value

SPCON Linear parameters for sediment re-entrainment 0.0001–0.01 1 0.001ainme

tIitrtod

spTficamm

SPEXP Exponent parameter for sediment re-entrUSLE C USLE C factor for land cover

a The percentage by which the original values changed.

hat the watershed area threshold has certain geomorphic limits.f the threshold value is too small and the generated river networks too dense, it will produce a large number of false channels. If thehreshold is too large it will lead to inconsistencies within the actualiver network. Therefore, the threshold should be selected based onhe unique characteristics of the study area. In this study, a thresh-ld value of 900 ha was ultimately selected and the watershed wasivided into 35 sub-watersheds (Fig. 2).

An HRU is part of a sub-basin, which includes an area with aingle soil type, land use type and the management pattern androperly selected HRUs can increase the precision of the simulation.here are two ways to divide an area into HRUs: (1) when using therst method, the advantage land use/soil method, the researcher

hooses the largest possible area with a combination of land usesnd soil types; that is, each sub-basin has only one HRU; (2) secondethod requires the division of each basin into a combination ofultiple land use/soil types, specifically multiple HRUs. This study

Fig. 2. The sub-watersheds produced by the SWAT model.

nt 1–2 2 1.43±20% 3 −5%a

used the second method and determined the land use and soil typethreshold values of 3% and 5%, respectively, so that the sub-basinwas ultimately divided into 355 hydrological response units.

3.3.2. Parameter sensitivity analysisSWAT model uses a large number of physical and chemical equa-

tions to describe the various physical and chemical phenomena.Sensitivity analysis of each input parameter of the model can revealwhich factors have a greater influence on runoff and sedimenttransport; the goal of sensitivity analysis is to differentiate theinfluence of volatility of the input parameters on the degree of vari-ability seen in the model output. Tables 2 and 3 present the sensitiveparameters for runoff and sediment yield simulation and calibratedvalues in this study.

3.3.3. Model performance indicatorsThe coefficient of correlation (R2) and the Nash–Sutcliffe coef-

ficient (Ens) (Nash and Sutcliffe, 1970) were used to test the linearrelationship between the measured and simulated values. R2 isdetermined as follows:

R2 =

⎡⎣

∑(Qm − Qm)(Qs − Qs)√∑

(Qm − Qm)2√∑

(Qs − Qs)2

⎤⎦

2

(3)

Ens is determined as follows:

Ens = 1 −∑

(Qm − Qs)2

∑(Qm − Qm)

2(4)

where Qm and Qs are the measured and simulated daily runoff,Qm and Qs are the average measured and simulated runoff. IfR2 > 0.6 and Ens > 0.5, the performance of SWAT model was arbi-trarily regarded as satisfactory.

3.3.4. Assessing the impacts of land use change on soil erosionand sediment yield

Land use change influences the water cycle significantlymainly by influencing the redistribution of moisture. To eliminateuncertainties related to changes in land use, such as defor-estation, farmland irrigation, urbanization, or traffic, an extremehypothetical method was used to simulate changes in the hydro-

logical processes such as runoff, sediment and evapotranspiration.Extreme scenario simulation is an important part of hydrologicaleffect research. It represents the possible range of change expectedin the hydrological response units and can eliminate interference of

S. Zhang et al. / Agricultural Water Management 133 (2014) 65– 73 69

Table 4Details of land use scenarios used in SWAT.

Scenarios Description

1 Hill drylands planted with wheat and hill paddy fieldsplanted with rice

2 All farmland planted with wheat3 All farmland planted with rice

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Table 6Simulation performance indicators for runoff and sediment yield using the SWATmodel.

Period Time Runoff Sediment

R2 Ens R2 Ens

Calibration1979 0.83 0.64 0.78 0.531979–1985 (daily) 0.85 0.65 0.86 0.671979–1985 (monthly) 0.84 0.63 0.82 0.60

TE

4 All farmland planted with corn5 All farmland planted with rape6 All farmland planted with sweet potatoes

ultiple factors in a hydrological system which allows researcherso determine each factor’s role in the hydrologic cycle.

The dominant crops in the agricultural portions of the Lizixiatershed are rice, rape, wheat, corn and sweet potatoes. To assess

he impacts of land use change on soil erosion and sediment yield,everal land use scenarios were developed (Table 4). Scenario 1epresents the current condition with the hill paddy fields plantedith rice and the hill drylands planted with wheat, at a ratio of 3:1.

cenarios 2–6 are extreme scenarios with all the farmland plantedith wheat, rice, corn, rape or sweet potatoes, respectively. In sce-arios 2–6, farmlands cover 88% of the entire watershed includingill paddy fields and hill drylands. With these six scenarios the

evels of sediment yield and soil erosion can be modeled underifferent land use situations and the intensity of soil erosion canlso be determined. From the results, one can determine whichrops have the best effect on reducing the sediment yield and soilrosion. Table 5 shows the erosion hazard classification (Ministryf Water Resources of the People’s Republic of China, 2008). Thistudy focused on how land use change contributes to reduce soilrosion in the Jialing River Basin, so the irrigation module was notsed. The crop was only moistened by precipitation. The methodsf irrigation and irrigated time have different effects on soil erosionFan et al., 2009).

.3.5. Quantify the effect of range of slopes on soil erosionSlope is an important factor influencing the slope erosion and

ediment yield. Under different slope, soil erosion intensity is nothe same. This study used four slope ranges; ranges 1, 2, 3, 4

odeled slopes of 0–5◦, 5–15◦, 15–25◦, and >25◦, respectively.ased on the DEM and land use map, the farmlands with slopeanges 1, 2, 3, 4 account for 18%, 45.9%, 14.3% and 8.8% in the wholeatershed, respectively. According to the Standards for classifica-

ion and gradation of soil erosion (SL 190-2007) (Ministry of Wateresources of the People’s Republic of China, 2007), slope is gener-lly divided into seven levels; 0–5◦, 5–10◦, 10–15◦, 15–20◦, 20–25◦,5–35◦ and >35◦. The Technical specification for comprehensive con-rol of soil and water conservation – Technique for erosion controlf slope land (GB/T 16453.1-2008) (Ministry of Water Resources ofhe People’s Republic of China, 2008) required that the farmlandsith slope >25◦ should return to forest or grass. The Party Cen-

ral Committee sets this policy as a significant strategic decision tomprove the environment and promote the sustainable develop-

ent of economy and society.A parameter, Ei, was introduced to assess the impact of different

ange of slopes on soil erosion. The parameter was determined as

ollows:

i = Si − S0

Ai(5)

able 5rosion intensity classification according to standards for classification and gradation of s

Level Tiny Mild M

Average erosion modulus (t/km2) <500 500–2500 2

Validation1984 0.94 0.85 0.88 0.721986–1995 (daily) 0.78 0.52 0.72 0.511986–1995 (monthly) 0.93 0.71 0.85 0.69

where Ei is the soil erosion increment (t/km2), S0 is the sedimentyield when all the farmlands are planted with wheat (t), Si is thesediment yield when the farmlands with slope range of 0–5◦, 5–15◦,15–25◦, and >25◦ plant a different crop (t) and Ai is the farmlandarea of different slope ranges (km2).

The soil erosion increment considers the influence of differentplanting varieties and planting area, and can quantitatively expressthe influence of a different range of slopes on watershed sedimentyield and soil erosion. This makes it easy to discover which rangeof slope contributes the most soil erosion based on the calculationof Ei; a greater value for Ei indicates higher levels of soil erosionare to be expected. This parameter also presents the variation ofsoil erosion per square meter; according to the comparison of Ei, itcan quantify which range of slopes returned to forest or grass canobtain greater economic benefits.

4. Results and discussion

4.1. Runoff and sediment calibration and validation

The SWAT model was set up and run for the entire Lizixiwatershed. Daily runoff and sediment yield data collected dur-ing 1975–1995 at the Anfu Gauging Station were used to calibrateand validate the SWAT model (Bryan and Christine, 2007). In theSWAT output files, the daily runoff and sediment yield data atsub-watershed outlet where Anfu Gauging Station was drainedwere used as the simulated value. Three time periods, 1975–1978,1979–1985 and 1986–1995 were used for the initial test, calibrationand validation periods, respectively. This allowed the determina-tion of the simulated and measured daily runoff and sediment yieldduring the calibration and validation periods (Figs. 3 and 4). Thesefigures only show a comparison of 1984 and 1993 as typical yearsof the calibration and validation periods, respectively, because theentire data series was too long to illustrate easily.

Table 6 lists the performance indicators of the SWAT model insimulating the runoff and sediment yield. In the calibration andvalidation periods, the first row shows the indicators of a typicalyear and second row shows the entire period indicators; both ofthem were based on daily measured and simulated data. The thirdrow also shows the indicators of the entire calibration and vali-dation periods, but it was based on the monthly measured andsimulated data. The monthly measured data originated from thedaily data and can be compared with the simulated data to further

verify the reliability of the SWAT model.

The simulated peak flow compared well with the measured peakflow and the base flow was overestimated during the entire year(Fig. 3). The R2 and Ens values for both the calibration and validation

oil erosion (SL 190-2007).

edium Intense Very intense Severe

500–5000 5000–8000 8000–15,000 >15,000

70 S. Zhang et al. / Agricultural Water Management 133 (2014) 65– 73

Fig. 3. Measured and simulated runoff.

Fig. 4. Measured and simulated sediment yield.

S. Zhang et al. / Agricultural Water Management 133 (2014) 65– 73 71

Table 7Soil erosion and sediment yield calculated with SWAT under six land use scenarios.

Scenario Sediment yield (103 t) Soil erosion (t/km2) Erosion intensity

1 621 890 Mild2 531 762 Mild3 1334 1913 Mild4 2549 3655 Medium

pflc

pSymrtbrsS

dsSo

4

sswry2ifttaco

Table 8The Ei values under different crop types in SWAT.

Crop types E1 E2 E3 E4

Corn 2110 2349 5479 9660Rape 2314 2636 6085 10,749

2 2 2

5 2819 4042 Medium6 1659 2379 Mild

eriods exceeded 0.6 and 0.5, respectively. An overestimated baseow may have resulted from the fact the SWAT model did notonsider agricultural irrigation of the paddy fields.

The calibration and validation of sediment yield were com-leted using only data collected during the rainy season (June toeptember) because the Anfu Gauging Station lacked sedimentield data during the season without rainfall (October to May). Theeasured and simulated sediment yield matched the occurrence of

ain events quite well (Fig. 4). In fact, the simulated values underes-imated the measured values in all years. This may have happenedecause the soil erosion equation used by SWAT model applied theunoff as the factor of erosion energy (Neitsch et al., 2005) and theimulated peak flow was less than the measured values. Also, theWAT model did not consider the effect of soil tillage.

The satisfactory values of R2 and Ens for the calibration and vali-ation periods indicated that the SWAT model performed well inimulating runoff and sediment at the watershed level and theWAT model adequately assessed the impacts of land use changen soil erosion and sediment yield.

.2. Effects of land use change on soil erosion and sediment yield

With the validated SWAT model, the annual mean soil ero-ion and sediment yield were simulated under different land usecenarios (Table 7; Fig. 5). One can conclude that monocroppedheat contributed the least to sediment yield, followed by

ice, sweet potato, corn and rape with annual mean sedimentields of 531 × 103 t, 1334 × 103 t, 1659 × 103 t, 2549 × 103 t and819 × 103 t, respectively; monocropping scenarios led to mild

ntensity erosion with the exceptions of medium intensity erosionor monocropped corn and rape. If the local governments desireo emphasize the reduction of sediment yield and soil erosion,

he planting of wheat, rice and sweet potatoes should be encour-ged with either monocropping or mixed planting of these threerops. For example, land use scenario 1 with the current 3:1 ratiof wheat and rice contributed 621 × 103 t sediment yield which fell

Fig. 5. Sediment yield and soil erosion u

Fig. 6. The soil erosion increment of corn and rape.

in between that of wheat and rice, so mixed planting can be appliedwithout considerable increase of erosion and sediment yield. How-ever, additional study is needed to determine which kind of mixedplanting is most effective in reducing soil erosion and sedimentyield. In addition, this study analyzes the soil erosion in differentcrops scenarios with the time unit of year, the land use with othercrop is not considered in this paper. Further study should be doneto consider the growing period of different crops.

4.3. Effects of range of slope on soil erosion and sediment yield

To quantify the relationship between slope and soil erosion, thesoil erosion increment Ei was defined. In this study, corn and rapewere used as examples (Table 8; Fig. 6). The spatial extent of the cul-tivated area used in slope ranges E1, E2, E3 and E4 was 142.21 km2,

358.84 km , 117.22 km and 60.48 km , respectively.

The numerical value of the Ei increased with an increase in slopefor both corn and rape. From slope range 1 to range 2, the vari-ation in the Ei was small. The variation in the Ei was large when

nder different land use scenarios.

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omparing range 2 to range 3 and it was similar to the variation ini from range 3 to range 4. The equation used in the SWAT modelo calculate soil erosion has a parameter named topographic factorLSUSLE). This parameter is proportional to the slope (Eq. (3)). Sedi-

ent yield increased as slope increased. Therefore, farmland withlopes >25◦ should be converted to forests or grasslands instead ofeing used for cropland, as an effective way to reduce soil erosion.o matter the farmland plants corn or rape, the ratio of E4 and E2as 4.11; the ratio of E4 and E3 was 1.76. These results indicated

hat the farmland with slope >25◦ was the most sensitive area toand use change. Returning 1 km2 farmland with slope >25◦ wasqual to returning 4.11 km2 farmland with slope range of 5–15◦ or.76 km2 farmland with slope range of 15–25◦ to forests or grass.o, the land use planner should focus on the farmland with slope25◦ in the Jialing Basin to reduce sediment yield.

. Conclusions

The Lizixi watershed was selected to assess the impacts of landse change on soil erosion and sediment yield in the Jialing Riverasin because of its representative topography, climate, and agri-ulture. It is also representative of the level and type of humaniving activities occurring in the basin. The SWAT model performedatisfactorily in simulating runoff and sediment yield. This showshis model can be used at the watershed level since it was accuraten simulating a long-term data run covering a large area.

Soil erosion has been a major environmental problem in theialing River Basin. It is important to identify which kind of cropesults in reduced soil erosion and sediment yield and to showhich areas are the most vulnerable to erosion. For monocrop-ing, wheat contributed the least to sediment yield, followed byice, sweet potatoes, corn and rape. Mixed planting was effective ineducing soil erosion and sediment yield, but additional research iseeded to determine which combination of crops will achieve theaximum effect, and growing period of different crops should also

e considered in future work.Slope was also a key factor influencing soil erosion and sedi-

ent yield in the study area. Soil erosion increment was definedn this study to quantify the degree of erosion. Among the 0–5◦,–15◦, 15–25◦ and >25◦ slope ranges in the watershed, the resultshow that farmland with slopes exceeding 25◦ suffered severe soilrosion. Steeply sloped lands would be best used as forestland orrassland rather than cropland even though these lands accountedor less than 10% of the entire watershed.

cknowledgments

This study has been supported by the National Natural Scienceoundation of China (51379076) and the 12th Five-Year Nationaley Technology R&D Program (2012BAB05B05). The authors wish

o thank International Scientific & Technical Data Mirror Site, Com-uter Network Information Center, Chinese Academy of Sciencesrovides the DEM data, thank Data Sharing Network of Earth Sys-em Science provides the land use maps, thank the Environmentalnd Ecological Science Data Center for West China, National Naturalcience Foundation of China provides the soil physical character-stics data and thank China Meteorological Data Sharing Serviceystem supports the meteorological data.

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