sg

aCollege of Water Conservancy and Hydropower Engineb State Key Laboratory of Soil Erosion and Dryland Farm712100, ChinacChangjiang River Scientic Research Institute, Wuhan 4dCollege of Resources and Environment, Huazhong Agric

a r t i c l e i n f o

Article history:

China, which are dominated by monsoon climate conditions. Mostof the rainfall in this region occurs during 1025 high-magnitudeevents that generate signicant stormow during the monsoonperiod from May to October (Lin et al., 2011). These heavy rains

sion and its associated problems have already degraded the landand water resources of China. Soil erosion affects an area of3.6 106 km2 in China, or approximately 37% of the countrys landarea (Ministry of Water Resources of PR China, 2009). Soil erosionhas become an important topic for local and national policy mak-ers. This discussion has led to an increasing demand for accurateinformation about watersheds and their hydrological processes.Such information should make it possible to delineate the targetzones in which soil and water conservation measures are likelyto be the most effective.

Corresponding author at: College of Resources and Environment, HuazhongAgricultural University, Wuhan 430070, China. Tel.: +86 27 87288249; fax: +86 2787671035.

Journal of Hydrology 484 (2013) 2637

Contents lists available at

Journal of H

elsE-mail address: shizhihua70@gmail.com (Z.H. Shi).land (the VIP and regression coefcient were 1.762 and 14.343, respectively) and forest (the VIP andregression coefcient were 1.517 and 7.746, respectively). The PLSR methodology presented in thispaper is benecial and novel, as it partially eliminates the co-dependency of the variables and facilitatesa more unbiased view of the contribution of the changes in individual land use types to changes instreamow and sediment yield. This practicable and simple approach could be applied to a variety ofother watersheds for which time-sequenced digital land use maps are available.

2013 Elsevier B.V. All rights reserved.

1. Introduction

Water quantity and quality problems are of great concernworldwide and particularly in watersheds located in subtropical

cause severe erosion and sediment export to rivers, which ulti-mately leads to degradation of soil resources and contributes tonegative off-site impacts downstream, such as ooding, pollutionand the siltation of water bodies and reservoirs. Extensive soil ero-Received 13 September 2012Received in revised form 5 January 2013Accepted 8 January 2013Available online 24 January 2013This manuscript was handled by GeoffSyme, Editor-in-Chief, with the assistance ofJohn W. Nicklow, Associate Editor

Keywords:Partial least squares regressionLand useStreamowSediment yieldSWAT0022-1694/$ - see front matter 2013 Elsevier B.V. Ahttp://dx.doi.org/10.1016/j.jhydrol.2013.01.008ering, Hohai University, Nanjing 210098, Chinaing on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences, Yangling, Shaanxi

30010, Chinaultural University, Wuhan 430070, China

s u m m a r y

Understanding how changes in individual land use types inuence the dynamics of streamow and sed-iment yield would greatly improve the predictability of the hydrological consequences of land usechanges and could thus help stakeholders to make better decisions. Multivariate statistics are commonlyused to compare individual land use types to control the dynamics of streamow or sediment yields.However, one issue with the use of conventional statistical methods to address relationships betweenland use types and streamow or sediment yield is multicollinearity. In this study, an integratedapproach involving hydrological modelling and partial least squares regression (PLSR) was used to quan-tify the contributions of changes in individual land use types to changes in streamow and sedimentyield. In a case study, hydrological modelling was conducted using land use maps from four time periods(1978, 1987, 1999, and 2007) for the Upper Du watershed (8973 km2) in China using the Soil and WaterAssessment Tool (SWAT). Changes in streamow and sediment yield across the two simulations con-ducted using the land use maps from 2007 to 1978 were found to be related to land use changes accord-ing to a PLSR, which was used to quantify the effect of this inuence at the sub-basin scale. The majorland use changes that affected streamow in the studied catchment areas were related to changes inthe farmland, forest and urban areas between 1978 and 2007; the corresponding regression coefcientswere 0.232, 0.147 and 1.256, respectively, and the Variable Inuence on Projection (VIP) was greaterthan 1. The dominant rst-order factors affecting the changes in sediment yield in our study were: farm-B. Yan a,c, N.F. Fang b, P.C. Zhang c, Z.H. Shi b,d,Impacts of land use change on watershedAn assessment using hydrologic modellin

journal homepage: www.ll rights reserved.treamow and sediment yield:and partial least squares regression

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8973 km and lies between the latitudes of 3130 and 3237 Nand the longitudes of 109110 to 110250E. It has a typical subtrop-

land use and management scenarios (Behera and Panda, 2006). Agreat number of SWAT applications have been used to study

ydrSoil erosion is controlled by many factors, including soil proper-ties, land use, climatic characteristics and topography (Wischmeierand Smith, 1978). Soil properties and topography can be consid-ered relatively constant in the short term, while changes in landuse and climatic features are the dominant variables (Wei et al.,2007). Soil erosion is largely determined by the absence of protec-tive land cover, whereas sediment export to rivers is determinedby on-site sediment production and the connectivity of sedimentsources and rivers (Bakker et al., 2008). Sediment export is also afunction of land use, since the sediment transport capacity is dif-ferent for different types of land cover (Van Oost et al., 2000). Ef-fects of land use changes include variations in surface roughness,the organic content of soil, the soil structure and inltration rate,and the hydraulic connectivity within a catchment (Fiener et al.,2011; Wei et al., 2009). These alterations often have important ef-fects on the spatial and temporal dynamics of hillslope hydrologyand sediment production, transport and delivery to rivers (Braudet al., 2001; Rey, 2003). Assessments of the hydrological responsesto land use changes and of the resulting sediment yields are valu-able for watershed management.

Many studies have discussed the inuence of land use changeson hydrology and sediment yield at different spatial and temporalscales (Van Rompaey et al., 2002; Siriwardena et al., 2006; Bakkeret al., 2008; Casali et al., 2010; Tang et al., 2011). Multivariate sta-tistics are commonly used to relate land use changes to the dynam-ics of streamow or sediment yield (Hao et al., 2004; Zhang andSchilling, 2006; Zhang et al., 2007b; Nie et al., 2011). However,one issue with the use of conventional statistical methods is theirinability to address the problem of multicollinearity in this context,particularly in cases with few observations. For example, ordinaryregression is hindered by limitations imposed by sample size(number of observations). In classical multiple regression proce-dures, low sample size relative to the number of predictors inatestype II errors; canonical correlation requires that the ratio of thenumber of predictors to the sample size be at least 0.0250.05(Carrascal et al., 2009). Multicollinearity and limited data availabil-ity often yield evidence of purely indirect relationships betweenland use types and streamow/sediment yield (Kim et al., 2002;Hao et al., 2004; Shi et al., 2007; Zhang et al., 2007b; Li et al.,2012). Therefore, we must investigate further how changes in eachland use type inuence streamow and sediment yield to achieve amore effective and more accurate means of conducting watershedmanagement and of predicting hydrological consequences of landuse changes.

The Danjiangkou Reservoir Area (DRA) in central China repre-sents a useful and crucial setting in which to assess the impactsof land use changes on streamow and sediment yield. In responseto the water deciencies in the northern part of the country, Chinaimplemented the Middle Route Project under the South-to-NorthWater Transfer Scheme in 2002. The Danjiangkou Reservoir onthe Han River, the largest tributary of the Yangtze River, is thewater source for the Middle Route Project and supplies 13.8 billionm3 of water annually to the North China Plain. To guarantee thequantity and quality of the transferred water, multiple nationalwatersoil conservation programs have been carried out sincethe 1990s to reduce soil erosion and nutrient loss (Li et al.,2009). Rapid land use changes have occurred in the DRA during re-cent decades. Therefore, the Upper Du Watershed within the DRAwas chosen as the case study area. The objectives of this studyare as follows: (1) to calibrate and validate the SWAT model interms of streamow and sediment yield for a gauging station inthe Upper Du Watershed; (2) to evaluate the impacts of land usechanges on streamow and sediment yield at the basin scale;

B. Yan et al. / Journal of Hand (3) to quantify the contribution of changes in individual landuse types on streamow and sediment yield using partial leastsquares regression at the sub-basin scale.hydrology and sediment yield in small or large catchments in dif-ferent regions of the world (see SWAT literature database: http://www.card.iastate.edu/swat_articles/). SWAT is a physically based,distributed, continuous daily time step parameter model. It is de-signed to predict the impact of land management practices onwater, sediment and agricultural chemical yields in large, complexwatersheds with varying soils, land use and management condi-tions over long periods of time. SWAT can be used to analyze smallor large catchments by discretizing them into sub-basins, whichare then further subdivided into hydrological response units(HRUs) each having homogeneous land use, soil types and slopes.The SWAT system embedded within GIS can integrate various spa-tial environmental data, including information about soil, land cov-er, climate and topographical features. A detailed description of themodel is available online in the SWAT documentation (http://swat-model.tamu.edu/).

3.1.2. Available input data and model setupFor the model setup, the SWAT model required input data for

topography, soil, and land use along with hydro-meteorologicaldata. The topographical information came from a Digital ElevationModel (DEM) with a resolution of 25 m 25 m purchased from theNational Geomatics Center of China (Fig. 2a). The soil data, includ-ing a soil type map (1:100,000) and information on related soilical monsoon climate. During the past 50 years, the mean annualair temperature has ranged from 12.4 to 18.4 C, and the annualrainfall has varied between 728 and 1480 mm. The catchment re-ceives a mean annual rainfall of 973 mm, of which the monsoonseason (JuneOctober) contributes more than 80%. The elevationvaries from 220 m at the Zhushan gaging station to 2833 m atthe highest point in the catchment. The topography of the wa-tershed is undulating. It is characterized by mountain ranges, steepslopes and deep valleys. According to the Chinese soil classicationsystem, the major soil types include yellowbrown soil, brown soil,Chao soil, and purple soil (National Soil Survey Ofce, 1992), whichcorrespond respectively to Alsols, Entisols, and Inceptosols in theUSA Soil Taxonomy (Soil Survey Staff, 1999). The dominant landuse types are forest and shrubland; villages, small towns and agri-cultural land are concentrated along the river. The major crops arecorn (Zea mays L.) and wheat (Triticum aestivum L.).

3. Methods

3.1. Modelling approach

3.1.1. The SWAT modelMany watershed scale hydrologic and water quality models

have been developed in recent years (Aksoy and Kavvas, 2005).The Soil and Water Assessment Tool (SWAT) is one of the mostsuitable models for simulating water and sediment yields under2. Study area

The Du River has its source in the piedmont of the Daba Moun-tains, and joins the Han River after a 354 km course with a meanslope of 4.8. In this study, the investigation area extends fromthe Upper Du Watershed to the Zhushan gaging station (Fig. 1).

The Upper Du Watershed has an area of approximately2 0 0

ology 484 (2013) 2637 27properties, were obtained from the Soil Survey Ofce of HubeiProvince (Fig. 2b). Land use data for the 4 years (1978, 1987,1999, and 2007) were obtained from the Changjiang River Water

ydr28 B. Yan et al. / Journal of HResources Commission (Fig. 3). The land use maps were generatedfrom Landsat images, which obtained from the Landsat archive(http://glovis.usgs.gov/). These images included Multi SpecialScanner imaging for 1978, Thematic Mapper imaging for 1987and 2007, and Enhanced Thematic Mapper imaging for 1999. Cli-mate data, which included daily data of precipitation, maximumand minimum temperature, solar radiation, humidity, wind speedand direction, and sunshine duration, were available from nineweather stations, eight of which were located within the Du Wa-tershed; the remaining one was located outside of the watershedborders (Fig. 1) but was situated close enough to the watershedfor its data to be considered by the model. Daily averages ofstreamow and sediment yield at the Zhushan gauging stationwas available from 1965 to 2010.

The current version, SWAT2009, was used to compile the SWATinput les. Based on the DEM, land use, and soil data, the Du Wa-tershed was divided into 107 subbasins (Fig. 2c), which in turnwere subdivided into 674 HRUs. The rainfall-runoff routing wascomputed using the SCS curve number method, and the channelrouting was calculated according to the variable storage coefcientmethod (Williams, 1969). The PenmanMonteith equation wasused to calculate the potential ET. The crops planted on agriculturalland in the Du Watershed were very diverse and alternated withinshort distances. It was not possible to reect these details in themodel because the resolution of the available land use map wastoo coarse to capture the small-scale pattern of the crops and be-cause SWAT does not allow for the inclusion of more than one cropin an HRU at the same time. Therefore, all agricultural areas in thewatershed were considered to have a wheat and corn rotation,with wheat growing during the cold season and corn growing dur-ing the warm season.

Fig. 1. Location of the study watershed, gology 484 (2013) 26373.1.3. Model calibration and validationThe model calibration and validation focused on improving

SWAT performance at the gauging station. For both runoff and sed-iment, the observed values for each measured time interval werecompared with the simulated values for that interval. In this man-ner, the whole hydrograph or sediment graph was tted by SWATrather than the model being simply optimized for peak or runoffvolume. The model was rst calibrated for streamow using the10-year time period from 1971 to 1980. Streamow was validatedusing the 10-year time period from 1981 to 1990. Sedimentcalibration and validation were performed for the periods 19711980 and 19811990, respectively. All of the daily data weresummarized into monthly intervals for the model evaluation pro-cedure. The model was run using a 6-year warm-up period from1965 to 1970. Table 1 lists the calibrated parameters with their ini-tial and nal values.

The performance of the model in simulating streamow andsediment was evaluated graphically and by using the NashSutcliffe efciency (ENS), Percent Bias (PBIAS), and coefcient ofdetermination (R2) values:

ENS 1Pn

i1Oi Pi2Pni1Oi Oave2

1

PBIAS Pn

i1Oi PiPni1Oi

100 2

R2 Pn

i1Oi Oave Pi PavePni1Oi Oave2

h i0:5 Pni1Pi Pave2h i0:5

8>:

9>=>;

2

3

auging station, and weather stations.

ydrB. Yan et al. / Journal of Hwhere n is the total number of data records, Oi is the observed value,Oave is the mean of the observed values, Pi is the simulated value,and Pave is the mean of the simulated values. Generally, the calibra-tion/validation performance of a SWAT model is considered accept-able when the R2 is greater than 0.5. Table 2 shows the performanceratings for two performance statistics, ENS and PBIAS, as suggestedby Moriasi et al. (2007).

3.1.4. Model applicationA xingchanging method (Wang et al., 2009; Tang et al.,

2011) was used to detect the effect of land use changes on stream-ow and sediment yield. The calibrated model was run for eachland use map (1978, 1987, 1999, and 2007), keeping the DEMand soil data constant, from January 1970 to December 2010. Thesimulated results were used to evaluate the impact of land use

Fig. 2. Map showing the Digital Elevation Model (DEM) (A), suology 484 (2013) 2637 29changes on changes in streamow and sediment yield at the basinscale and to quantify the contribution of the changes within indi-vidual land use types to changes in streamow and sediment yieldat the sub-basin scale.

3.2. Partial least squares regression

The changes in streamow and sediment yield across two sim-ulations developed using land use maps for 2007 and 1978 wererevealed to be related to land use changes using partial leastsquares regression (PLSR), which was used to quantify the contri-bution of changes in individual land use types to streamow andsediment yield at the sub-basin scale. The independent variableswere the changes in the six land use types (farmland, forest, shrub-land, grassland, urban, and barren). The dependent variables were

bbasins (B), and soil types (C) in the Upper Du Watershed.

Fig. 3. Land use maps for 1978 (A), 1987 (B), 1999 (C) and 2007 (D) in the Upper Du Watershed.

Table 1Parameters used to calibrate streamow and sediment yield at the Zhushan gauging station.

Parameter Denition Min. value Max. value Calibrated value

Parameters used to calibrate streamowESCO Soil evaporation compensation factor 0 1 1EPCO Plant water uptake compensation factor 0 1 1SURLAG Surface runoff lag time 0 10 2

GW_DELAY Groundwater delay 0 500 10GW_REVAP Groundwater revap 0.02 0.2 0.05ALPHA_BF Baseow alpha factor 0 1 0.5

SOL_AWC Available water capacity of the soil layer 0 1 0.2CH_N1 Mannings n value for tributary channels 0.01 0.5 0.1CH_N2 Mannings n value for main channel 0.01 0.5 0.02CN2 SCS curve number 35 98 39 (Forest)

48 (Shrubland)68 (Grassland)81 (Farmland)92 (Barren)89 (Urban)

Parameters used to calibrate sedimentPRF Peak rate adjustment factor for sediment routing 0 2 2CH_COV Channel cover factor 0.001 1 1CH_EROD Channel erodibility factor 0.05 0.6 0.08SPCON Linear parameters for calculating the channel sediment rooting 0.0001 0.01 0.008SPEXP Exponent parameter for calculating the channel sediment routing 1 2 0.5

30 B. Yan et al. / Journal of Hydrology 484 (2013) 2637

changes in the streamow and sediment yield. A preliminary anal-ysis (as illustrated using the bivariate scatterplots in Fig. 4) had al-ready shown that many land use types were co-linear. Whether ornot the sample comes from a normally distributed population wastested using the AgostinoPearson K2 test. The AgostinoPearsonK2 test determines skewness to quantify the asymmetry of the dis-tribution and kurtosis to quantify the shape of the distribution(Agostino et al., 1990). Wherever necessary, the predictors werelog transformed to achieve a normally distributed population.

PLSR is a robust multivariate regression method that is appro-priate when the predictors exhibit multicollinearity. The methodcombines the features of principal component analysis and multi-ple linear regression (Abdi, 2007). The basic PLSR algorithm is notdescribed in this paper, but further information on PLSR can be ob-tained from Abdi (2007) and Carrascal et al. (2009). One of theinteresting features of PLSR is that the relationships between thepredictors (in our case, land use types) and the response function(the mean annual streamow and sediment yield) can be inferredfrom the weights and regression coefcients of individual predic-tors in the most explanatory components. Thus, it is possible to

determine which land use types most strongly interact withstreamow and sediment yield.

Two separate PLSR models were constructed to identify themain land use types that control streamow and sediment yield.To overcome the problem of over-tting, the appropriate numberof components of each PLSR model was determined by cross-vali-dation to achieve an optimal balance between the explained vari-ation in the response (R2) and the predictive ability of the model(goodness of prediction: Q2). In PLSR modelling, the importanceof a predictor for both the independent and the dependent vari-ables is given by the variable importance for the projection (VIP).The terms having large VIP values are the most relevant forexplaining the dependent variable. The cross-validated goodnessof prediction (Q2) and percentage of variance explained for the re-sponse variables (streamow and sediment yield), as well as thecross-validated root mean squared error (RMSECV) as the differ-ence between the predicted and observed values of each individualpass, were determined for each model. The regression coefcientsof the PLSR models were used to show the direction of the relation-ship between the changes in individual land use types and stream-ow or sediment yield. SIMCA-P+12.0 (Umetrics AB, Sweden) wasused to perform PLSR, and SPSS 20 (IBM SPSS Inc., Chicago, USA)was used to perform the statistical analysis of the dataset.

4. Results and discussion

4.1. Calibration and validation of SWAT

The simulated and measured monthly streamow and sedimentyield values in the calibration period (1971/011980/12) and in the

Table 2General performance ratings for the recommended statistics (Moriasi et al., 2007).

Performancerating

ENS PBIAS (%)

Streamow Sediment

Very good 0.75 < ENS 6 1.00 PBIAS < 10 PBIAS < 15Good 0.65 < ENS 6 0.75 10 6 PBIAS < 15 156PBIAS < 30Satisfactory 0.50 < ENS 6 0.65 15 6 PBIAS < 25 306PBIAS < 55Unsatisfactory ENS 6 0.50 PBIASP 25 PBIASP 55

B. Yan et al. / Journal of Hydrology 484 (2013) 2637 31Fig. 4. Bivariate scatterplot matrix of selected land use types with histograms positionedis occurrence frequency of sub-basins. Bold numbers are for p < 0.05.along the diagonal. The X axis is changes in corresponding land use type, and Y axis

validation period (1981/011990/12) are compared in Figs. 5 and6. The consistency of the simulated and measured values is clear.The ENS and R2 values for the monthly calibration and validationare listed in Table 3. All of the ENS and R2 values for streamoware greater than 0.8, and the PBIAS values are in the range of10%, which suggests very good model performance (Moriasiet al., 2007). Fig. 6 indicates the adequate calibration and valida-tion results for the entire range of sediment yields. The statisticalcomparison between the measured monthly sediment yield andsimulation results shows good agreement. The ENS, PBIAS and R2

values show adequate results for both the calibration and the val-idation periods.

Although the overall performance of the model is satisfactory,as shown in Table 3, there was a signicant difference across themonthly sediment values for 1977/07, 1980/08, 1982/07, 1987/07 and 1989/07. The sediment estimation using the model showsthat although the trends with regard to simulated and measuredsediment yield were similar for most years, the sediment yieldwas underestimated during most summers. The high sediment dis-charge events were not well matched with the corresponding pre-dictions, which tended to be too low. A possible reason for thesediscrepancies could be the limitations of the SCS-CN method,which, when used with the SWAT model, does not consider theduration and intensity of precipitation, instead using meand dailyrainfall depths as the SWAT inputs (Nie et al., 2011). However,rainfall events mainly included high-intensity, short-duration rain-storms in the Upper Du Watershed. In practice, high-intensity andeven short-duration rainfall could generate more sediment thanwas indicated by the model based on daily rainfall (Xu et al.,2009). Overall, the consistency between the results of the simula-

generated using the optimal parameters were successfully em-ployed to evaluate the hydrological consequences of land usechanges.

4.2. Impacts of LULC changes on streamow and sediment at the basinscale

The land use changes during the past four decades in the UpperDu Watershed are shown in Table 4. Forest, grassland and shrub-land decreased before 1999. Conversely, the area devoted to farm-land increased from 882.8 km2 in 1978 to 1217.6 km2 in 2007, withan annual growth rate of 1.4%. However, after 1999 the forest be-gan to increase while the amount of farmland decreased from13.6% to 5.8%. The urban region gradually expanded from 0.8% to1.4% during the entire study period, while the proportion of barrenland was relatively stable (0.30.4%) from 1978 to 1999 and thenincreased to 0.7% by 2007. This result indicates that governmentpolicy had a strong impact on land use. China initiated the House-hold Responsibility System in 1978 and adopted a market-direc-ted economic system in 1992 (Li, 1999). As a result, thegovernment allowed farmers to make all decisions about land re-sources and production, and deforestation and cultivated hillsideareas were then very common in rural regions. However, 1999was a turning point with regard to land use changes and coincidedwith the beginning of the Grain to Green Program, which was thelargest land retirement program implemented in China and whichused a public payment scheme that directly engaged millions ofrural households as the core agents of the project implementation(L et al., 2012).

The observed annual streamow for the year 1978, 1987, 1999

32 B. Yan et al. / Journal of Hydrology 484 (2013) 2637tion and the measured values, as well as the high ENS and R2 valuesand low absolute values for PBIAS, indicated that the calibratedmodel could describe monthly streamow. Thus, the SWAT modelsFig. 5. Comparison between the measured and simulated monthly strand 2007 was 595.7, 618.2, 510.6 and 525.7 mm, and sedimentyield was 630.3, 697.1, 510.9 and 543.8 t km2, respectively. Thedynamics of annual streamow and sediment yield resulted fromeamow values for the calibration (A) and validation periods (B).

B. Yan et al. / Journal of Hydrology 484 (2013) 2637 33coupling of land use change and climate variability. The annual ba-sin values for streamow and sediment yield simulated by usingthe land use maps are shown in Table 4. Compared to the landuse baseline from 1978, the mean annual streamow over the wa-tershed was 6.9 mm higher in 1987 and 34.9 mm higher in 1999(for increases of 1.3% and 6.5%, respectively), whereas it was24.6 mm lower in the 2000s. The mean annual sediment yield alsoincreased gradually from 599.4 t km2 for the land use in 1978 to700.1 t km2 for the land use in 1999 and then decreased to512.0 t km2 for land use in 2007. The changes in streamow

Fig. 6. Comparison between the measured and simulated monthly se

Table 3Criteria for examining the accuracy of the model calibration and validation.

Index Calibration (19711980) Validation (19811990)

Streamow Sediment yield Streamow Sediment yield

ENS 0.88 0.67 0.87 0.64PBIAS 6.4 19.8 5.1 32.1R2 0.94 0.84 0.92 0.81

Table 4Proportion of land use, mean annual basin values for streamow and sediment yield, and

Time period Farmland (%) Forest (%) Shrubland (%) Grassland (%) Wat

1978 9.8 70.9 10.2 7.6 0.41987 10.2 70.4 10.4 7.3 0.41999 13.6 69.3 9.4 5.9 0.32007 5.8 76.2 9.5 6.1 0.3

19781987 +0.4 0.5 0.2 0.3 019871999 +3.4 1.1 +1.0 1.4 0.119992007 7.8 +6.9 0.1 +0.2 019782007 4.0 +5.3 0.7 1.5 0.1

a Sediment yield.and sediment yield matched the land use dynamics, particularlyfor forest and farmland (Table 4). A very strong positive correlationwas observed between streamow and sediment yield and the pro-portional of farmland (with R2 values of 0.99 and 0.98, respec-tively); in contrast, a negative relationship between theproportion of forest and streamow and sediment yield was ob-served (R2 is 0.83 and 0.88, respectively). On a regional scale, overa 1- to 10-year period, the water gains resulted mainly from pre-cipitation, and the losses were mainly due to runoff and evapo-transpiration (Oki and Kanae, 2006). The mean annual waterbalance can be expressed as follows: Q = P ET DS, where Q, P,ET, and DS are the annual streamow, precipitation, evapotranspi-ration, and change in water storage, respectively. The change in Qbetween the two periods could be described as

DQ Q2 Q1 P2 P1 ET1 ET2 DS2 DS1 4The amount of precipitation was constant and the change in the

water storage during the two periods was similar due to the use ofthe xing-changing method. The change in Q due to land use wasessentially the change in the evapotranspiration between the two

diment yields for the calibration (A) and validation periods (B).

changes in these values for the Upper Du Watershed.

er (%) Urban land (%) Barren land (%) Streamow (mm) SYa (t km2)

0.8 0.3 536.1 599.40.9 0.4 543.0 613.61.1 0.4 571.0 700.11.4 0.7 511.5 512.0

+0.1 +0.1 +6.9 +14.2+0.2 0 +28.0 +86.5+0.4 +0.3 59.5 187.9+0.6 +0.4 24.6 87.4

ydrperiods, i.e., DQ = ET1 ET2. The annual ET losses from seasonalcrops are generally smaller than the ET losses from trees becauseperennial vegetation transpires throughout the spring, summerand fall, whereas seasonal crops, such as corn and soybeans, donot transpire until the middle of the growing season. For example,the single-crop ET coefcients (Kc) reported by the United Nations(FAO, 1998) for various vegetation types vary substantially. Theinitial, mid-season, and late-season Kc values for trees (0.50, 1.27,0.67) show much greater ET in the initial and late growing seasonscompared to eld corn (0.00, 1.20, 0.35) and wheat (0.00, 1.15, 0.5).Therefore, it seems that when forest is changed to farmland, result-ing in decreased vegetation coverage, The change would also even-tually reduce evapotranspiration, adversely affect soil waterstorage and decrease rainfall inltration, thereby increasingstreamow. We show that increasing streamow is mainly a func-tion of stormow, which is itself a result of land use changes. Inadjacent basins, when the forest cover decreased by 1%, the annualstreamow and sediment yield increased by approximately3.5 mm and 0.67 t km2, respectively (Zhang et al., 2007a).

One concern is that the changes in the streamow componentsand sediment yield may have been caused by simulation errors. Forexample, the changes in mean basin sediment yield from 1978 to1987, as shown in Table 4 (2.4%), could be less than the simulationerror because the percentage of the bias in the calibration modelwas 19.8% and the corresponding gure for the validation periodwas 32.1% (Table 3). However, much higher variations in stream-ow and sediment yield existed at the sub-basin scale. The in-crease in farmland area at the sub-basin scale was 42.2%, whichwas much higher than the mean increase of 0.4% at the basin scale(Table 4). Similarly, the increases in sediment yields in the sub-ba-sins were as high as 241.4 t km2, which was much higher than the14.2 t km2 increase in the entire basin (Table 4). The relativechange in the 1987 sediment yield relative to the 1978 value foreach sub-basin ranged from 67.3% to 44.2%. However, the highcoefcient of determination for the changes in farmland area andsediment yields at the sub-basin scale (R2 = 0.94, n = 107) suggeststhat the increases in sediment yields in the sub-basins were actu-ally mainly a function of the increases in farmland area rather thanof simulation errors. In addition, the relative change in streamowin 1978 with respect to the 1987 values ranged from 26.2% to36.3%. The high variations in the streamow at the sub-basin scaleindicate that the changes in streamow related to the land usechanges were much higher than the level of model accuracy, evenfor the very small land use changes during that occurred between1978 and 1987. Thus, the simulation results obtained using the cal-ibrated model were used to evaluate the impacts of land usechanges on streamow and sediment yield.

4.3. Contribution of changes in individual land use types onstreamow and sediment

The spatial distribution of the changes in the six land use types(i.e., farmland, forest, shrubland, grassland, urban and barren), thesimulated streamow and the sediment yield between 1978 and2007 are shown in Figs. 7 and 8. Changes in land use mainly oc-curred in the middle and lower stretches in the northern part ofthe watershed. The most signicant increases in streamow andsediment yield also mainly occurred in the lower stream, largelymatching the spatial distribution pattern of the farmland and ur-ban expansion. The decrease in stormow and sediment yield inthe northern portion of the watershed spatially corresponded tosub-basins in which the majority of the farmland was replacedby forest (Fig. 7).

34 B. Yan et al. / Journal of HA summary of the two PLSR models constructed separately forstreamow and sediment yield is provided in Table 5. For thestreamow model, the prediction error decreased with an increas-ing number of components, and the minimum RMSECV was ob-tained with two components. An additional increase in thenumber of components generated a higher prediction error, sug-gesting that the other components were not strongly correlatedwith the residuals of the predicted variable (Onderka et al.,2012). The rst component explained 74.1% of the variance inthe dataset in terms of the changes in streamow (Table 5). Theaddition of the second component led the models to cumulativelyexplain 83.5% of the total variance. The addition of more compo-nents to the PLSR models did not substantially improve the vari-ance explained (Table 5). Because the PLSR weights were linearcombinations of the original variables that dened the scores, theycould be used to describe the quantitative relationship betweenthe predictors and results (Abdi, 2007). The rst component ofthe streamow model (Table 6) was dominated by farmland andurban areas on the positive side and by forest on the negative side,whereas the second component was dominated by farmland andurban and barren land on the positive side and by shrubland onthe negative side. The low weight values for grassland indicatedits relatively low importance in affecting streamow. Althoughthe PLSR weights (Table 6) indicated the importance of individualchanges in land use type for the observed streamow and sedi-ment yield, a more convenient and comprehensive expression ofthe relative importance of the predictors could be obtained byexploring their VIP values. For streamow, a higher VIP valuewas found for changes in forest, farmland and urban land(VIP > 1), followed by the percentage of shrubland, barren landand grassland (from 0.775 to 0.217). Predictors with VIP values be-low 1 are considered to be of minor importance for predictions. Asexpected, changes in farmland and urban land encouraged greaterstreamow; they had positive regression coefcients (0.232 and1.256, respectively). Whereas forest had a negative regression coef-cient (0.147) and was associated with lower streamow. In thecurrent study, urbanization was identied as a strong factor affect-ing the changes in streamow, presumably due to the increase inimpervious surface areas. A similar conclusion was reported byNie et al. (2011) pertaining to a study on the Upper San Pedro Wa-tershed, USA.

For the sediment yield model, the minimum RMSECV was ob-tained using three components, the rst of which explained80.6% of the variance in the changes in sediment yield. The addi-tion of the second and third components enabled the model tocumulatively explain 84.9% and 86.3% of the variance in thechanges in sediment yield, respectively. The addition of furthercomponents did not signicantly increase the explained variance(Table 5). The changes in farmland appeared to dominate the rstand second components of the sediment yield PLSR model,whereas forest changes dominated on the negative side of the rstcomponent (Table 6). For the sediment yield model, higher VIP val-ues were observed with the changes in the farmland and forest(VIP > 1), followed by shrubland, barren land, grassland and urbanland (the VIP values ranged from 0.610 to 0.214). Farmland andforest also had larger positive or negative regression coefcients(14.343 and 7.746, respectively).

Our goal was not to develop a prediction model but rather toidentify the main land use dynamics that affect changes in stream-ow and sediment yield. In practice, land use data exhibit co-line-arity because an increase in the percentage of one type necessarilydecreases the percentage of one or more of the other types (Kinget al., 2005). In this context, the PLSR methodology is benecialand novel because it makes it possible to partially eliminate co-dependency among the variables and encourages a more unbiasedview of the contribution of changes in individual land use types to

ology 484 (2013) 2637changes in streamow and sediment yields. However, the pre-sented approach also has limitations. The PLSR only allows a gen-eral insight into the relationships between different predictors and

ydrB. Yan et al. / Journal of Hchanges in streamow and sediment yield, whereas more detailedinformation about spatial land use patterns cannot be accountedfor using this methodology. The spatial distribution patterns of

Fig. 7. Spatial distribution of land use

Fig. 8. Spatial distribution of streamow and sediology 484 (2013) 2637 35land use can exert a signicant inuence on runoff and sedimenttransport processes at different scales (Fu et al., 2009). It is impor-tant to quantify the effects of spatial land use patterns on sediment

changes between 1978 and 2007.

ment yield changes between 1978 and 2007.

meatw

Y

4.058 0.294 0.115 0.078 0.132

a The bold-faced numerical values are larger than 0.3 and indicate that the PLSR com

ydryield to develop effective soil erosion control through spatial plan-ning for land use. Therefore, future research efforts in this areashould focus on the relationships between spatial land use patternsand changes in streamow and sediment yield. Such studies maybe small-scale, bottom-up experimental investigations or larger-Table 5Summary of the PLSR models for all subbasins. The RMSECV (cross-validated root(goodness of t) and Q2 (cross-validated goodness of prediction) were calculated for a

Response variable Y R2 Q2 Component % Of explained variability in

Streamow 0.84 0.73 1 74.12 9.43 1.14 0.25 0.0

Sediment yield 0.86 0.84 1 80.62 4.33 1.44 0.25 0.1

Table 6VIP values and PLSR weights.a

Predictors Streamow

Regression coefcients VIP W[1] W[2]

Farmland 0.232 1.394 0.596 0.428Forest 0.147 1.469 0.633 0.048Shrubland 0.069 0.775 0.219 0.655Grassland 0.038 0.217 0.041 0.112Urban 1.256 1.056 0.431 0.499Barren 0.331 0.453 0.096 0.437

36 B. Yan et al. / Journal of Hscale, top-down conceptual model applications that will yield amore in-depth understanding of the processes in question.

5. Conclusions

The contributions of changes in land use types to changes instreamow and sediment yield in the Upper Du Watershed wereevaluated using hydrological modelling and PLSR. The impacts ofland use changes on changes in streamow and sediment yieldwere evaluated and quantied.

The major land use changes that affected streamow in ourstudy catchments were changes to farmland, forest and urbanareas from 1978 to 2007. The VIP values for these land types, whichare greater than 1, were considered to indicate great importancefor streamow dynamics, and regression coefcients of the rela-tionship to farmland, forest and urban areas were 0.232, 0.147and 1.256, respectively. The dominant rst-order factors for sedi-ment yield in our study were farmland (the VIP and regressioncoefcient were 1.762 and 14.343, respectively) and forest (theVIP and regression coefcient were 1.517 and 7.746, respec-tively). Our results indicated that changes in grassland did not ex-ert a signicant inuence on either streamow or sediment yield.

The approach used in this study simply determined the contri-butions of land use changes to changes in streamow and sedi-ment yield, providing quantitative information that would allowstakeholders and decision makers to make better choices regardingland and water resource management. This approach could be ap-plied to a variety of watersheds in instances in which time-sequenced digital land use information is available. Using this tool,researchers could predict the hydrological consequences of landuse changes.Acknowledgments

Financial support for this research was provided by the NationalNatural Science Foundation of China (41071190), the National Sci-ence and Technology Supporting Programs (2012BAC06B03), the

2.914 0.214 0.019 0.319 0.23410.005 0.309 0.055 0.094 0.819

ponents are mainly loaded on the corresponding variables.n squared error), Q2cum (cross-validated goodness of prediction) per component, R2

o-component streamow model and a three-component sediment yield model.

Cumulative explained variability in Y (%) RMSECV (mm or t km2) Q2cum

74.1 3.23 0.67883.5 2.66 0.73584.6 2.68 0.72084.8 2.69 0.69284.8 2.74 0.67380.6 103.4 0.78984.9 97.1 0.82886.3 92.0 0.83786.5 94.2 0.83786.6 95.6 0.832

Sediment yield

Regression coefcients VIP W[1] W[2] W[2]

14.343 1.762 0.740 0.520 0.2087.746 1.517 0.639 0.079 0.0153.922 0.610 0.167 0.804 0.651

ology 484 (2013) 2637Program for New Century Excellent Talents in University (NCET-10-0423), and the Fundamental Research Funds for the CentralUniversities (2011PY001). The authors would also like to thankProfessor David Warrington from Institute of Soil and Water Con-servation of CAS & MWR for his help on improving English.

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Impacts of land use change on watershed streamflow and sediment yield: An assessment using hydrologic modelling and partial least squares regression1 Introduction2 Study area3 Methods3.1 Modelling approach3.1.1 The SWAT model3.1.2 Available input data and model setup3.1.3 Model calibration and validation3.1.4 Model application

3.2 Partial least squares regression

4 Results and discussion4.1 Calibration and validation of SWAT4.2 Impacts of LULC changes on streamflow and sediment at the basin scale4.3 Contribution of changes in individual land use types on streamflow and sediment

5 ConclusionsAcknowledgmentsReferences