Effect of land-use changes on nonpoint source pollution in the Xizhi River watershed, Guangdong, China

HYDROLOGICAL PROCESSESHydrol. Process. (2012)Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/hyp.9368

Effect of land-use changes on nonpoint source pollution in theXizhi River watershed, Guangdong, China

Kai Xu,1,2 Yunpeng Wang,1* Hua Su,1,2 Jingxue Yang,1,2 Lili Li1,2 and Cang Liu31 State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China

2 Graduate University of Chinese Academy of Sciences, Beijing 100049, China3 Hunan Technical College of Water Resources and Hydro Power, Changsha 410131, China

*CGeofE-m

Co

Abstract:

The Annualized Agricultural Non-point Source (AnnAGNPS) pollution model has been widely used to assess and predict runoff, soilerosion, sediment and nutrient loadingwith a geographic information system. This article presents a case study of the effect of land-usechanges on nonpoint source (NPS) pollution using the AnnAGNPS model in the Xizhi River watershed, eastern Pearl River Delta ofGuangdong province, China. The land-use changes in the Xizhi River watershed between 1998 and 2003 were examined using themultitemporal remote sensing data. The runoff, soil erosion, sediment transport and nutrient loading 1998 and 2003 were assessedusing AnnAGNPS. The effects of land-use changes on NPSwere studied by comparing the simulation results of each year. Our resultsshowed that (i) theNPS loadings increasedwhen forest and grass land converted into paddy, orchard and farmland land, and populationsize and gross domestic product size as well as the usage amounts of fertilizer and pesticide in the entire watershed were firmlycorrelated with the NPS loadings; (ii) the land-use change during fast urbanization in particular when other land types were convertedinto the development land and buildup land led to increasing of NPS pollution; and (iii) urban land expansion showedmore importanteffects on total organic carbon (TOC) loading compared with nitrogen and phosphorus loadings. Copyright © 2012 John Wiley &Sons, Ltd.

KEY WORDS land-use changes; nonpoint source pollution; AnnAGNPS model; Xizhi River

Received 27 October 2011; Accepted 20 April 2012

INTRODUCTION

Nonpoint source (NPS) pollution has grown into global andregional environmental issue and has been the mostwidespread environmental degradation problem in recentyears (Thornton et al., 1999). Because of the nature of NPSand the limitation of experiments and field measurements,its management is highly dependent on spatial simulationmodelling. These simulation models have become animportant technique commonly used to deal with NPSpollution problems associated with spatial uncertainty(Shamshad et al., 2008).Environmental models provide an efficient way to

quantitatively evaluate pollutant loading from NSP as wellas natural processes occurring at a watershed scale and aidsfor the control andmanagement of NSP (Pullar and Springer,2000; Borah and Bera, 2003). Environmental models,greatly developed since the 1980s, have provided possiblesolutions by using the capability to model NSP processesexactly, including processes of rainfall–runoff, soil losses,nutrients and sediment transportation (Huang and Hong,2010). These models range from simple screening andplanning models, such as the Universal Soil Loss Equation(Wischmeier and Smith, 1978), to complex hydrologicalassessment models, such as the Areal Non point Source

orrespondence to: Yunpeng Wang, State Key Laboratory of Organicochemistry, Guangzhou Institute of Geochemistry, Chinese AcademySciences, Guangzhou 510640, China.ail: [email protected]

pyright © 2012 John Wiley & Sons, Ltd.

Watershed Environment Response Simulation (ANSWERS)(Beasley et al., 1980), the Erosion Productivity ImpactCalculator (Williams et al., 1982), the Simulator for WaterResource in Basins (SWRB) (Williams et al., 1985), theWater Erosion Prediction Project (WEPP) (Nearing et al.,1989), the Agricultural Non-point Source (Young et al.,1989), the Hydrological Simulation Program–Fortran(Donigian et al., 1995), the Soil and Water AssessmentTool (SWAT) (Arnold et al., 1998), the European SoilErosion Model (Morgan et al., 1998), the AnnAGNPS(Bingner and Theurer, 2003) and the Loading SimulationProgram inC++ (LSPC) (Shen et al., 2005). Detailed reviewsof these models were carried out by Borah and Bera (2003)and Merrit et al. (2003). These models differ in terms ofcomplexity, the processes considered and the data requiredfor model calibration and validation. In general, there is nosingle best model for all applications (Shamshad et al., 2005).Model selection depends mainly on the goal of the

simulation, the scale of the studied area, the availability ofdata, the expected accuracy and the temporal and financialcosts (Grizzetti et al., 2005; Kovacs, 2006; Kliment et al.,2008). In medium-sized areas (102–104 km2) semiempi-rical models are often applied, combining physically basedand empirically derived simulation algorithms (Borah andBera, 2003). These are often called conceptual models(Beven, 2001) and enable continuous long-term predic-tions of runoff, soil erosion, sediment transport and otherhydrological processes in large river basins and theirsubareas (Kliment et al., 2008). Examples of conceptual

K. XU ET AL.

erosion models include AnnAGNPS (Bingner andTheurer, 2003), Hydrological Simulation Program–Fortran (Bicknell et al., 1996) and SWAT (Arnold et al.,1998). Because they are free, available and linked to ageographic information system (GIS) system, AnnAGNPS(Bingner and Theurer, 2003) and SWAT (Arnold et al.,1998) are frequently used.The Annualized Agricultural Non-point Source

(AnnAGNPS) pollution model has been widely usedfor predictions of runoff, soil erosion, sediment transportand nutrient load (Polyakov et al., 2007). The model hasbeen tested over a wide range of environments in Europe(Pekarova et al., 1999), North America (Perrone andMadramootoo, 1997; Yuan et al., 2001), Australia(Baginska et al., 2003), Africa (Leon et al., 2003) andChina (Liu et al., 2008; Huang and Hong, 2010). Thepopularity of AnnAGNPS has recently increased becauseof its applicability and integration with GIS capabilities,which significantly simplified its use (Tsou and Zhan,2004). The mainstream researches are focusing on evalu-ating the performance and suitability of the AnnAGNPSmodel in assessing runoff, soil erosion, sediment transportand nutrient loading. However, a comparative study ofthe effect of land-use change on NPS with AnnAGNPS inthe same watershed over a specific period has yet to be

Year

Figure 1. The rainfall erosivity from 1997 to 2007 in theXizhi River watershed

Figure 2. Location of the study watershed showing

Copyright © 2012 John Wiley & Sons, Ltd.

less conducted. In one certain watershed, the changes oflandform, climate and soil type are negligible to NPS.However, during a small period (e.g. 1998–2003), the mainfactors that affected NPS are the changes of land use andrainfall. We estimated the rainfall erosivity from 1997 to2007 in the Xizhi River watershed using the daily rainfallerosivity model proposed by Zhang and Fu (2003). Theresults are shown in Figure 1. It is clear that there is smallinfluence from the rainfall between 1998 and 2003 on NPSin the Xizhi River watershed.The objectives of this article were (i) to examine the

land-use changes in the Xizhi River watershed between1998 and 2003 by using multitemporal land-use mapsfrom two different years, GIS method and socioeconomicdata; (ii) to present the predictions of the simulationresults of the 2 years, in terms of runoff, soil erosion,sediment transport and nutrient loading in Xizhi Riverwatershed; and (iii) to compare the simulation results ofthe 2 years and to discuss the effects of land-use changeson NPS of the study area.

MATERIALS AND METHODS

Description of study watershed

The Xizhi River watershed, located in northeasternPearl River Delta of Guangdong province in China(114�330–115�260E, 22�300–23�230N), covers approximate-ly 2.7� 104 ha (Figure 2). It has a subtropical monsoonclimate; the annual average air temperature is approximately21.7 �C and the annual average rainfall is approximately1805mm.The reason we selected the Xizhi River watershed as a

case study area because the Xizhi River is the mostimportant branch of Dong River in the Pearl River Delta,and Dong River is the water source for drinking as well asindustrial and agricultural use for local and Hong Kongpeople. The fast economic growth rate of most of cities inthe Dong River watershed reaches up to 15% in the past

Xizhi River watershed boundary and water flow

Hydrol. Process. (2012)DOI: 10.1002/hyp

Table I. Input data formats of major parameters for AnnAGNPS model

Data Data format Data source

DEM Grid (cell size 90� 90m) Shuttle Radar Topography Mission–DEMSoil map Vector map (polygon) Soil surveys at 1:250 000 scaleLand-use map (1998 and 2003) Vector map (polygon) Landsat TM data by unsupervised classification

and visual interpretationSoil parameter database Table (text file) Soil surveys in Huidong county of Guangdong provinceCrop database Table (text file) Annual statistical data from local governmentDatabase of agriculturalmanagement operation

Table (text file) Annual statistical data from local government

Daily precipitation Table (text file) Guangdong meteorological stationMinimum and maximum dailytemperature, dew point, cloud cover,wind direction and speed(1998 and 2003)

Table of daily values Guangdong meteorological station

LAND-USE CHANGES ON NPS

decade, and the average economic density reaches12 000 000 US dollars/km2. This area has become one ofthe most important areas of intensive farming and fruit

Table II. Socioeconomic data of study area

1998 2003 % Change

Population 667 174 698 441 4.69%Plantation (ha) 81 701 107 546 31.63%GDP (dollars) 106 085 167 717 58.10%Agricultural GDP (dollars) 20 183 29 209 44.72%Food supplies (1000 kg) 217 687 286 495 24.02%Fertilizer (1000 kg) 20 934 70 927 238.81%Pesticide (1000 kg) 385 916 137.92%

Figure 3. Land-use maps of Xizhi River watershed in 199


industry. At the same period, this area experienced rapidurbanization, and there are more than 35 million residentsliving the four biggest cities in the east of Guangdongprovince including Guangzhou, Shenzhen, Dongguan andHuizhou as well as the rural areas. The regionaldevelopment particular in the past two decades has causedtremendous changes to the land use, potentially affectingthe nonpoint pollutions.

Description of AnnAGNPS models

The AnnAGNPS pollution model is a distributed-parameter, continuous simulation, watershed scale modeldeveloped jointly by the US Department of Agriculture–Agricultural Research Service and the Natural Resources

8 and 2003 derived from Landsat TM and ETM+ data


Table III. Land-use information (ha) of 1998 and 2003

1998 2003 Changes % Changes

URB 766.0 1 014.9 248.9 32.49%FOR 187 426.7 163 831.1 �23 595.6 �12.59%WAT 2 278.3 1 793.3 �485.0 �21.29%DIS 432.9 186.8 �246.1 �56.85%PAD 24.8 103.6 78.8 317.11%GRA 69 967.0 9 669.8 �60 297.2 �86.18%FAR 4 130.8 5 701.6 1 570.8 38.03%ORC 7 578.5 92 069.5 84 491.0 1115%DEV 1 896.2 1 330.5 �565.7 �29.83%

URB, area of urban; FOR, area of forest; WAT, area of water; DIS, area of disk pond; PAD, area of paddy field; GRA, area of grass field; FAR, area offarmland; ORC, area of orchard; DEV, area of development.

Figure 4. Major land-use spatial changes of the study watershed between 1998 and 2003. (a) Major land-use spatial changes from forest and grassfield into paddy field, farmland and orchard. (b) Major land-use spatial changes from development, water, disk pond and forest into urban, paddy field

and farmland

K. XU ET AL.

Copyright © 2012 John Wiley & Sons, Ltd. Hydrol. Process. (2012)DOI: 10.1002/hyp


Conservation Service (Bosch et al., 1998) to aid decisionmaking on land management. The model is an improvedversion of the event model AGNPS (Young et al., 1989),which can simulate runoff, soil erosion, sediment transportand nutrient loads because of precipitation, irrigation andsnowmelt (Polyakov et al., 2007). In this study,AnnAGNPSversion 4.00 was used.The AnnAGNPS is composed of several modules that

require intensive input preparation. The basic modellingcomponents include those of hydrology, sediments, nutrientand pesticide transport (Pease et al., 2010). A watershed inthe model is represented by a system of cells and pointsources connected by a channel network through which thewater and pollutants are routed. Cells are defined on thebasis of topographic homogeneity. Cells and reaches aredelineated, and their topographic properties determinedusing TOPAGNPS operate on daily time steps with aminimum simulation period of 1 year. Rainfall excess isdetermined on the basis of soil water balance for constantsubdaily time steps. AnnAGNPS uses the Soil ConservationService CN method (SCS, 1972) to determine overlandrunoff and the TR-55 method to calculate peak flow(SCS, 1986). Themodel accounts for lateral subsurface flowand tile drainage. Runoff in channels is calculated usingManning’s equation. Soil erosion is predicted by the

Table IV. Simulation results of the forest, g

Runoff(mmyear�1)

Soil erosion(mg year�1)

Sedime(mg year

98For 892.2 31 144.3 4 414.03For 826.1 28 191.2 3 996.Changes �66.1 �2 953.1 �418.% Changes �7.41% �9.48% �9.Average % changes �9.74%98Gra 859.7 591 708.6 74 181.03Gra 829.5 91 843.4 12 425.Changes �30.2 �499 865.2 �61 755.% Changes �3.51% �84.48% �83.Average % changes �84.27%98Orc 825.6 1 150 523.2 138 595.03Orc 935.0 23 129 483.1 2 684 094.Changes 109.4 21 978 959.9 2 545 499.% Changes 11.70% 1 910% 1 837%Average % rate of changes 1858%

98For, loading of the forest type in 1998; 03For, loading of the forest type ingrass field type in 2003; 98Orc, loading of the orchard type in 1998; 03Orc

Table V. Comparison of the si

FGD

Runoff (mmyear�1) 96.3Soil erosion (mg year�1) 502 818.3Sediment (mg year�1) 62 173.5Nitrogen (kg year�1) 430 739.6Phosphorus (kg year�1) 1 243 083.6Organic carbon (kg year�1) 2 804 303.0 1

FGD, sum of the decreased loading of the forest and grass field types; OI, i


Revised Universal Soil Loss Equation (Renard et al.,1997), sediment delivery is predicted by the Hydrogeo-morphic Universal Soil Loss Equation (Theurer and Clarke,1991) and sediment transport in reaches is based on theEinstein deposition equation (Einstein and Chien, 1954).Nutrient dynamics includes the fate of nitrogen, phosphorusand organic carbon and is based on the Erosion ProductivityImpact Calculator (Williams, 1995) and the GroundwaterLoading Effects of Agricultural Management Systems(Leonard et al., 1995) algorithms.Overall, AnnAGNPS requires input parameters in 34

data categories (climate, soil, crop characteristics, man-agement operations, etc.). Many of these parameters areoptional or can be estimated from data already available,making it possible to use the model in data-deficientsituations. Daily climate input includes seven weatherparameters (minimum and maximum daily temperature,precipitation, dew point, cloud cover, wind direction andspeed), which can be either historical data or data generatedusing a climate generation program, like generationof weather elements for multiple applications (GEM)(Johnson et al., 2000). An input editor and a user-friendlyGIS interface are used to build program input files. Outputcan be obtained for any location (cell, reach or pointsource) on the watershed and at user-specified times.

rass field and orchard types of the 2 years

nt�1)

Nitrogen(kg year�1)

Phosphorus(kg year�1)

Organic carbon(kg year�1)

5 816 674.7 2 587 184.6 237 047.15 736 546.3 2 327 392.6 213 557.10 �80 128.4 �259 792.0 �23 490.047% �9.81% �10.04% �9.91%

0 416 327.2 1 158 785.8 3 288 809.15 65 716.1 175 494.1 507 996.25 �350 611.1 �983 291.7 �2 780 812.925% �84.22% �84.86% �84.55%

1 159 938.0 379 546.0 6 921 618.44 3 184 802.8 6 412 079.2 149 756 770.03 3 024 864.8 6 032 533.2 142 835 151.6

1 891% 1 589% 2064%

2003; 98Gra, loading of the grass field type in 1998; 03Gra, loading of the, loading of the orchard type in 2003.

mulation results in Table IV

OI Changes % Changes

109.4 13.1 13.57%21 978 959.9 21 476 141.6 4271%2545 499.3 2 483 325.8 3 994%3024 864.8 2 594 125.2 602%6032 533.2 4 789 449.6 385%

42 835 151.5 140 030 848.5 4 993%

ncreased loading of the orchard type.


K. XU ET AL.

Data preparation

The most important input data formats and types forAnnAGNPS model are shown in Table I. Digital maps(Digital Elevation Model data (DEM), land-use and soilmaps) were processed with a GIS interface to create therequired model input text files. The AnnAGNPS Arcviewinterface (Bingner and Theurer, 2003), which is used toinput the databases and meteorological data, is anextension of the ESRI Arcview (version 3.3) GIS software.In this study, DEM was sourced from the Shuttle

Radar Topography Mission, and its cell size was90� 90m. The soil map (1:250 000), provided by theEcology Environment and Soil Institute of GuangdongProvince after the field surveys, contained seven mainsoil texture classes and was available for the entire studyarea. The land-use map was sourced from LandsatThematic Mapper (TM) and Enhanced Thematic Mapper+ (ETM+) data using a supervised classification method,which has been published by Fan et al. (2008). In thisarticle, nine main land-use classes were identified, whichincluded urban areas, forest, water, disk pond, paddyfield, farmland, grass field, orchard and developed areas(Fan et al., 2008). The developed area was a typical

Figure 5. Simulation results of runoff, soil erosion, sediment, nitrogen, ph


land-use type in Pearl River Delta and other industrialareas in China, which was composed of a block ofground level land mainly from agricultural or noncon-struction land after primary development (Fan et al.,2007). The classification accuracy had been assessed byFan et al. (2008), and the results showed that the overallaccuracy of land-use classification of 1998 and 2003 inthis study reaches 91.6% and 86.5% (Fan et al., 2008).Most of the input parameters were sourced frommeasured data or were estimated on the basis of thepublished literature and the references data. The dailymeteorological data were sourced from the Climate andWeather Information Center of Guangdong Province.The rainfall distribution type was determined using themathematical function according to the study ofStraková (1998), which made it possible to estimatethe Revised Universal Soil Loss Equation rainfall factor.The socioeconomic data of study area are shown

in Table II. These were sourced from the GuangdongStatistical Yearbook. Between 1998 and 2003, there wereincreases in plantation area, population size and grossdomestic product (GDP) and a marked increase in fertilizerand pesticide use.

osphorus and organic carbon in 1998 by using the AnnAGNPS model



RESULTS AND DISCUSSION

Land-use changes of the study area between 1998and 2003

The land-use information is presented in Figure 3 andTable III, and the most important land-use spatial changesof the study watershed in the 2 years are presented inFigure 4.From Table III, we found that the orchard land area

increased by 84 491.0 ha over the period, from 7578.5 ha in1998 to 92 069.5 ha in 2003. The grass land area decreased60 297.2 ha, from 69 967.0 ha in 1998 to 9669.8 ha in 2003.The forest land area decreased 23 595.6 ha, from187 426.7 ha in 1998 to 163 831.1 ha in 2003. Themagnitude of the changes in these three land-use types ismuch larger than the other types. The results clearlyindicated that orchard changed mainly from grass fieldand forest during this period.The expansion of urban, paddy field and farmlandmay be

related to industrial development as well as populationgrowth during this period. According to fieldwork, urban,paddy field and farmland changed mainly from water, diskpond, development and a negligible amount of forest.

Figure 6. Simulation results of runoff, soil erosion, sediment, nitrogen, ph


It can be observed in Figure 4a that forest and grass fieldchanged into orchard mainly in the north of the middlereaches and south of the downriver reaches of Xizhi River,and the change was distinct along the river way.From Figure 4b, it can be found that urban, paddy field

and farmland changed from water, disk pond, developmentand a negligible amount of forest mainly in the north andmiddle of the downriver reaches of Xizhi River.

Comparison of the simulation results of 1998 and 2003and discussion of the effects on land-use changes

The statistics of the simulation results on the forest type,the grassfield type and the orchard type of the 2 years aswellas the difference are presented in Tables IV and V. Theaverage proportional changes in soil erosion, sedimenttransport and nutrient load of the forest type, the grass fieldtype and the orchard type were �9.74%, �84.27% and1858% (Table IV). At the same time, the proportionalchanges in the area of the three types were �12.59%,�86.18% and 1115% (Table III). This suggests that theforest type contributed the least to soil erosion, sedimenttransport and nutrient load considering its area change, the

osphorus and organic carbon in 2003 by using the AnnAGNPS model


K. XU ET AL.

orchard type contributed the most and the grass fieldtype had intermediate effects. However, the pattern isdifferent in terms of the runoff values. The proportionalchanges in runoff of the forest, grass field and orchard typewere �7.41%, �3.51% and 11.70% (Table IV). Theproportional changes in area of the forest, grass field andorchard types were �12.59%, �86.18% and 1115%(Table III). An examination of both Tables III and Table IVsuggests that the forest type exerted a greater influence onthe changes in runoff than the grass field type and theorchard type. Nevertheless, the increase in runoff value ofthe orchard type was larger than the sum of the decreases inrunoff value of the forest and grass field type.The proportional changes were 13.57% for runoff and

602% and 385% for nitrogen and phosphorus, respectively(Table V). The proportional changes in soil erosion andsediment were approximately 4000%, and the value fororganic carbon was approximately 5000%. This shows thatthere are different effects on the values of runoff, soilerosion, sediment transport and nutrient load when the land-use types changed from forest and grass filed to orchard.As previously mentioned, the land-use map (Figure 4a)

showed that the forest and grass field conversion into

Table VI. Simulation results on the water, disk pond, develop

Runoff(mmyear�1)

Soil erosion(mg year�1)

Sed(mg

98Wat 1527.4 99 927.5 12 203Wat 1363.6 59 732.5 7 3Changes �163.8 �40 195.0 �4 9% Changes �10.72% �40.22% �Average % changes �37.65%98Dis 1453.7 2 636.0 203Dis 1237.4 927.9Changes �216.3 �1 708.1 �1% Changes �14.88% �64.80% �Average % changes �63.99%98Dev 1424.1 10 163.7 1 103Dev 1265.5 647.8Changes �158.6 �9 515.9 �1 0% Changes �11.14% �93.63% �Average % changes �92.82%98Urb 1257.4 4 162.2 403Urb 1629.9 6 562.1 7Changes 372.5 2 399.9 2% Changes 29.62% 57.66%Average % changes 50.18%98Pad 857.5 1 293.9 103Pad 959.0 2 889.5 3Changes 101.5 1 595.6 1% Changes 11.83% 123.32%Average % changes 150.63%98Fam 811.5 10 348.1 1203Fam 916.1 11 214.1 14Changes 104.6 866.0 1% Changes 12.89% 8.37%Average % changes 7.10%

98Wat, loading of the water type in 1998; 03Wat, loading of the water type indisk pond type in 2003; 98Dev, loading of the development type in 1998; 03Dtype in 1998; 03Urb, loading of the urban type in 2003; 98Pad, loading of th98Fam, loading of the farmland type in 1998; 03Fam, loading of the farmla


orchard mainly took place in the north of the middle reachesand south of the downriver reaches of Xizhi River between1998 and 2003. Correspondingly, the stimulation resultsshowed that the condition of soil erosion, sedimenttransport, nutrient load of nitrogen, phosphorus and organiccarbon became poorer in the same areaswhere the forest andgrass field changed into orchard (Figures 5 and 6).The statistics of the simulation results on the water type,

the disk pond type, the development type, the urban type, thepaddy field type and the farmland type of the 2 years as wellas the difference is presented in Tables VI and VII. BecauseFigure 4b showed that the area of forest that changedinto urban, paddy field or farmland was small enough todisregard, this forest type is not shown in Tables VI andVII.A slight increase in the runoff was observed as the land-

use changed while the other types of pollution all decreased(Tables VI and VII). The increases in the runoff value thatare observed in Tables V and VII were relatively close toeach other. According to Figures 4b–6, an obvious increasein runoff volume mainly took place in an area wherewater, disk pond and development had changed into urban,paddy field and farmland land use, especially north of thedownriver and around the north boundary.

ment, urban, paddy field and farmland types of the 2 years

imentyear�1)

Nitrogen(kg year�1)

Phosphorus(kg year�1)

Organic carbon(kg year�1)

52.8 61 219.9 64 880.5 544 184.444.1 39 781.3 41 356.3 344 452.808.7 �21 438.6 �23 524.2 �199 731.640.06% �35.02% �36.26% �36.70%

65.8 5 997.4 9 376.2 8 969.393.2 2 859.6 2 946.8 2 753.772.6 �3 137.8 �6 429.4 �6 215.664.94% �52.32% �68.57% �69.30%

25.1 28 959.8 39 360.6 33 352.383.1 2 505.1 1871.7 2 915.842.0 �26 454.7 �37 488.9 �30 436.592.62% �91.35% �95.24% �91.26%

79.1 17 396.6 13 056.0 17 978.322.5 19 419.7 15 447.6 38 194.343.4 2 023.1 2 391.6 20 215.050.82% 11.63% 18.32% 112.45%

73.5 190.8 642.0 3 872.543.7 484.9 1 774.5 11 664.470.2 294.1 1 132.5 7 791.998.11% 154.09% 176.42% 201.21%

93.8 12 478.8 56 490.7 35 237.430.0 13 801.1 58 872.3 35 870.136.2 1 322.3 2 381.6 632.710.52% 10.60% 4.22% 1.80%

2003; 98Dis, loading of the disk pond type in 1998; 03Dis, loading of theev, loading of the development type in 2003; 98Urb, loading of the urban

e paddy field type in 1998; 03Pad, loading of the paddy field type in 2003;nd type in 2003.


Table VII. Comparison of the simulation results in Table VI

WDDD UPFI Changes % Changes

Runoff (mmyear�1) 538.7 578.5 39.8 7.39%Soil erosion (mg year�1) 51 418.9 4 861.5 �46 557.4 �90.55%Sediment (mg year�1) 6 123.4 549.8 �5 573.6 �91.02%Nitrogen (kg year�1) 51 031.1 3 639.4 �47 391.7 �92.87%Phosphorus (kg year�1) 67 442.5 5 905.7 �61 536.8 �91.24%Organic carbon (kg year�1) 236 383.8 28 640.6 �207 743.2 �87.88%

WDDD, sum of the decreased loading of the water, disk pond and development types; UPFI, sum of increased loading of the urban, paddy field andfarmland types.


Figures 5 and 6 adopted the same ranking system.However, the value of pollution change in TableV is severalorders of magnitude higher than the value in Table VII. Thepollution changes demonstrated in Figures 5 and 6 wereclearly observed in the areas where forest and grassland wasconverted into paddy field, farmland or orchard (Figure 4a).In other areas where water, disk pond and developmentchanged into urban, paddy land and farmland land use, thepollution change was not so evident.Although Tables VI and VII show that the water type

contributed largely to soil erosion, sediment loading andnutrient loading, it is the development type that was mostinfluential to the alternation of these pollutions, with anaverage rate of �92.82%. Table VI also highlights that asthe urban area increased, the rate of organic carbon loadsreached 112.45%, which was much higher than otherpollution types with an average value of 50.18%.

CONCLUSIONS

This article presents a case study of Xizhi River watershedbased on the simulation results from an AnnAGNPSpollution model. Predictions of runoff, soil erosion,sediment transport and nutrient loading are compared for 2years, and the relationship with land-use change thatoccurred between these years are discussed. On the basisof this study of land-use changes and NPS in the XizhiRiver watershed, China, the following conclusionswere obtained:

(1) A large amount of forest and grass field was convertedinto paddy field, orchard and farmland to meet demandsarising from human population growth and GDPincrease. This led to a large use of fertilizer andpesticide, which increased the amount of NPS pollution.

(2) According to the analysis of the simulation results, theforest contributed the least to soil erosion, sedimenttransport and nutrient load considering its change inarea, the orchard contributed the most and the grassfield type had intermediate effects. The pattern isdifferent for the changes in runoff values. Landchanging from forest and grass to orchard had thegreatest effects on sediment and organic carbonloading and the smallest effect on runoff, whereaseffects on soil erosion, nitrogen and phosphoruswere intermediate.


(3) During urbanization, changes in land use lead toincreased levels of pollution. The developmentland-use type is most influential for changes in soilerosion, sediment loading and nutrient loading. Thissuggests that pollution can be limited by restricting thearea of development land use.

(4) Changes in urban land use have more important effectson organic carbon loading compared with nitrogen andphosphorus loading. Essentially, urban lifestyles pro-duce a large amount of organic carbon.

ACKNOWLEDGEMENTS

This study was financially supported by the ChinaNational 863 program (grant no. 2006AA06A306) andthe Guangdong NSF (grant no. 8151064004000013). Theauthors thank the China Meteorological Data SharingService System for sharing the meteorological productsand the Center for EarthObservation andDigital Earth, CASfor sharing remote sensing data. They also thank theanonymous reviewers and the associated editor of thisjournal for their constructive comments, which have greatlyimproved the manuscript. This is contribution No. IS-1502from GIGCAS.

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Documents

Effect of land-use changes on nonpoint source pollution in the Xizhi River watershed, Guangdong, China