Research ArticleScenario-Based Impact Assessment of Land UseCover andClimate Changes on Watershed Hydrology in Heihe River Basinof Northwest China

Feng Wu1 Jinyan Zhan1 Hongbo Su2 Haiming Yan1 and Enjun Ma3

1State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Beijing Normal UniversityBeijing 100875 China2Key Laboratory of Water Cycle amp Related Land Surface Processes Institute of Geographic Sciences and Natural Resources ResearchCAS Beijing 100101 China3School of Mathematics and Physics China University of Geosciences Wuhan 430074 China

Correspondence should be addressed to Jinyan Zhan zhanjybnueducn

Received 9 August 2014 Accepted 8 October 2014

Academic Editor R B Singh

Copyright copy 2015 Feng Wu et alThis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

This study evaluated hydrological impacts of potential climate and land use changes in Heihe River Basin of Northwest China Thefuture climate data for the simulation with Soil andWater Assessment Tool (SWAT) were prepared using a dynamical downscalingmethodThe future land uses were simulatedwith theDynamic LandUse System (DLS)model by establishingMultinomial LogisticRegression (MNL) model for six land use types In 2006ndash2030 land uses in the basin will experience a significant change with aprominent increase in urban areas a moderate increase in grassland and a great decrease in unused land Besides the simulationresults showed that in comparison to those during 1981ndash2005 the temperature and precipitation during 2006ndash2030 will changeby +08∘C and +108 respectively The land use change and climate change will jointly make the water yield change by +85while they will separately make the water yield change by minus18 and +98 respectively The predicted large increase in futureprecipitation and the corresponding decrease in unused land will have substantial impacts on the watershed hydrology especiallyon the surface runoff and streamflow Therefore to mitigate negative hydrological impacts and utilize positive impacts both landuse and climate changes should be considered in water resource planning for the Heihe River Basin

1 Introduction

Climate and land useland cover (LULC) changes are amon-gst the greatest global environmental pressures resulting fromanthropogenic activities both of which greatly impact thehydrological cycle [1ndash3] Their impact on the hydrologicalcycle at the basin scale has become an important researchissue in hydrology community owing to the increasingly seri-ous water scarcity [4 5] The hydrological response of water-sheds to climate and LULC changes is an important issueof water resource planning and management [6 7] andthe potential impacts of LULC change on the hydrologicalcycle must be considered by water resource managers [8 9]For example the LULC changes due to urbanization anddeforestation can alter the hydrological processes and lead to

change of flood frequency and annual mean discharge byimpacting the evapotranspiration soil infiltration capacityand surface and subsurface flow regimes [8 10 11] whileclimate change can alter the flow routing time and peak flows[10 12] It is crucial to the long-term water resource planningand management to better understand the potential impactsof climate and LULC changes on the runoff and streamflow inbasins [9 13] In particular effective water resource manage-ment under changing conditions requires reliable informa-tion about flows and modes that can be used to simulate flowregimes under different scenarios of changing land use andclimate [6]

Separation of impacts of climate and LULC changes onthe hydrological cycle is of great importance to improving theland use planning and water resource management [10 14]

Hindawi Publishing CorporationAdvances in MeteorologyVolume 2015 Article ID 410198 11 pageshttpdxdoiorg1011552015410198

2 Advances in Meteorology

especially in arid and semiarid regions where the climatechange may significantly affect the hydrological cycle [15]The effects of LULC and climate changes on the streamflowaremore evident in the arid and semiarid regions One typicalexample is the Heihe River Basin in Northwest China whichis characterized by limited water resources and special hydro-climatic and physiographic conditions [16] Understandingthe hydrological responses to potential climate change is veryimportant for developing sustainable water resource man-agement strategies in this region However the impacts ofurbanization and deforestation on the hydrological cycle inarid and semiarid regions have been rarely documented [17ndash19] Overall there is still very limited understanding of theseparate as well as combined impacts of LULC and climatechanges on regional water and energy cycles and thereforemore in-depth research is needed especially in the arid andsemiarid regions [17]

There are many studies about the hydrological impacts ofland use change or climate change at the basin scale andmostof them were conducted with a hydrological model based ona series of land use data extracted from satellite images [9 2021] For example the impacts of land use change scenariosin the Wutu watershed North Taiwan were assessed usingthe conversion of a land use model (CLUE-s) and a gener-alized watershed loading functions model [16] A Soil WaterAssessment Tool (SWAT) and multiple General CirculationModels (GCMs) were used to investigate the relationshipbetween climatic and hydrological changes in the UpperMississippi River [22] Most of these studies assume nochange in LULC [4 23] but the impacts of climate changeon hydrology vary among regions and should be investigatedwith regional climate change scenarios [4] Besides thehydrological impacts of LULC change also vary with theclimatic conditions [9] For example water balance variablesmight add or subtract the impacts of climate change undervarying land cover conditions In particular the regionalLULC change can offset or magnify the changes in globalaverage temperature and can significantly alter the impactsassociated with global warming [17 24] In addition somestudies about the combined effects of climate and LULCchanges on streamflow showed that climate change wasgenerally more significant than LULC change in determiningthe basin hydrological response [25ndash27] For example climatedominates the changing streamflow in the Xinjiang RiverBasin of Poyang Lake China [25] However the hydrologicalcycle in a basin is a complex process influenced by climate andthe physical properties of the catchment and human activitiestogether [4 5] The complexity of these factors complicatesthe separation between effects of land use and climaticvariability on streamflow [26 28] Therefore it is still a chal-lenge to distinguish the effects of LULC change from that ofconcurrent climate variability [14]

This study aims to separate the impacts of climate andLULC changes on the hydrological cycle in the Heihe RiverBasin under future scenarios to provide some useful referenceinformation that can be used to improve the water resourcemanagement and guarantee the sustainable developmentThe climatic data predicted by General Circulation Models(GCMs) under RCP 45 scenario were used to represent

the climate change scenarios for 2006ndash2030 and the land usedata simulated with the Dynamic Land Use System (DLS)model were used to represent the land use change scenariosThe future hydrological cycle was simulated with the SWATbased on the scenario data of climate change and land usechange the hydrological impacts of which were analyzed bycomparing the simulation results under different scenariosThe results of this study can provide valuable information forguiding futurewater resourcemanagement in theHeiheRiverBasin as well as other arid and semiarid regions in China

2 Methods

21 Study Area The Heihe River Basin is the second largestinland river basin in China which lies between 37∘431015840ndash42∘411015840N and 97∘231015840ndash102∘721015840E with a total area of 12796 thou-sand km2 This basin expands across Qinghai ProvinceGansu Province and Inner Mongolia Autonomous Regionin Northwest China (Figure 1) With a total length of 821 kmthe Heihe River is divided into the upper middle and lowerreaches where the natural and socioeconomic characteristicsdiffer significantly For example the average annual precipita-tion is between 200 and 500mm less than 200mm and lessthan 50mm in these reaches respectively while the annualevaporation ranges from 700mm in the upper reach to morethan 3000mm in the lower reach [29] Besides the annualaverage temperature is 94∘C over the last 30 years and thisbasin enjoys a dry continental climate The altitude rangesfrom 869 to 5542m with an average of 1778mThemain landcover types are desert (5715 of total basin area) mountains(3316) and oasis (819) [30] The ecosystems from theupper reach to the lower reach are linked by the hydrologicalcycle but the hydrological cycle has significantly changeddue to the land use change and climate change in the pastdecades For example about 65 of the irrigation water inthe middle reach was extracted from the river runoff whichgreatly influences the hydrological cycle of the whole basinTherefore a detailed and integrated simulation analysis of thewater resources is critical and urgent for better water resourcemanagement in the Heihe River Basin

22 Data for Model Simulation The spatial data (ie topog-raphy soil and land use) historical climate data and hydro-logical data for the watershed were first prepared for theSWAT model and DLS model The topography was repre-sented with the 90m resolution digital elevation model(DEM) of Shuttle Radar TopographyMission (SRTM) (httpsrtmcsicgiarorg) [31] The soil data including texturedepth and drainage attributes were from the HarmonizedWorld Soil Database (HWSD) supplied by the Environmentaland Ecological Science Data Center for West China (West-DC) (httpwestdcwestgisaccn) The historical land usedata including 25 land use types which were derived fromLandsat TMETM images were provided by Data Centerof Chinese Academy of Sciences (CAS) [32] In particularthe glacier data were obtained from WestDC (httpwestdcwestgisaccn) and land use properties were directly obtain-ed from the SWAT model database (Table 1) The historicalhydrological data for SWATmodel calibration and validation

Advances in Meteorology 3

1340 5293

(km)

Yingluoxia

Qilian

Zhengyixia

Zhangye

Elevation (m)

99∘E 100

∘E 101∘E

99∘E 100

∘E 101∘E

40∘N

39∘N

38∘N

40∘N

39∘N

38∘N

100∘E90

∘E80∘E70

∘E ∘E110∘E120

100∘E90

∘E80∘E

∘E110 ∘E120

∘E130∘E140

50∘N

40∘N

30∘N

20∘N

50∘N

40∘N

30∘N

20∘N

10 20 30 400

Tibet

Xinjiang

Qinghai

Inner Mongolia

Gansu

Sichuan

Yunnan

Hunan

Jilin

Heilongjiang

Hebei

Hubei

HenanShaanxi

Shanxi

Guizhou

JiangxiFujian

Guangdong

Shandong

Liaoning

Jiangsu

ZhejiangChongqing

Ningxia

Taiwan

BeijingTianjin

ShanghaiAnhui

Hainan

Guangxi

(a)

Gray meadow soilGray desert soilSierozemFelty soilsMeadow SolonchakAeolian soil

Soil types

99∘E 100

∘E 101∘E

99∘E 100

∘E 101∘E

39∘N

38∘N

39∘N

38∘N

Cold desert soilChestnut soilBrown calcic soilIrrigated desert soil

Gray desert soilGray-brown desert soil

Chernozem

(b)

Yingluoxia

Qilian

Zhengyixia

Zhangye

Slope

30 30015(km)

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E 102

∘E

39∘N

38∘N

39∘N

38∘N

0ndash22ndash55ndash15

15ndash2020ndash25gt25

(c)

Sunan

Qilian

Gaotai

Shandan

Linze

Minle

Ganzhou

SunanSuzhou

Sunan

Jinta

Forest landGrasslandWater

Built-up areaUnused landCultivated land

99∘E 100

∘E 101∘E

99∘E 100

∘E 101∘E

39∘N

38∘N

39∘N

38∘N

(d)

Figure 1 The upper and middle Heihe River Basin (a) digital elevation model (b) soil type (c) slope and (d) land use in 2010

include the river flow data from four hydrological stationsThe hydrological observation data including annual datain Heihe River Basin during 1980ndash2010 were obtained fromthe hydrological yearbook provided by WestDC The riverflow data used for the model calibration and validation wereprovided by the Data Center of CAS The historical dailyweather data during 1980ndash2010 were collected from ChinaMeteorological Administration (CMA) including daily

precipitation maximum and minimum temperatures solarradiation humidity wind speed and wind direction from 13weather stations in or near the Heihe River Basin

23 Simulation of Land Use Changes with the DLS ModelThe DLS model is a set of applications used to simulate thechanging process of land use system and is an effective tool forsimulation of spatial-temporal land use changes to assist land

4 Advances in Meteorology

Table 1 Data used and sources

Data type Data sources Scale Description

DEM Shuttle Radar Topography Mission(SRTM) 90m Elevation

Land use Data Center of Chinese Academy ofScience 1 100000 The classification system contains 25

categories

Soil WestDC (httpwestdcwestgisaccn) 1 1000000Some parameters were calculated using a(Soil-Plant-Atmosphere-Water) Field andPond Hydrology model

Weather China Meteorological Administration Daily 5 weather stationsHydrological observation Hydrological yearbook Daily 4 stations

River flow Data Center of Chinese Academy ofScience 1 250000 River network diversion

Glacier WestDC (httpwestdcwestgisaccn) 1 100000 The attributes include width length anddepth

management [33] The DLS model consists of three modulesa spatial regression module that identifies the relationshipsbetween land uses and the influencing factors a scenarioanalysis module of land use changes that determines the landdemands at the regional level a spatial disaggregationmodulethat allocates land use changes from a regional level to thedisaggregated grid cells [34 35]

The DLS model simulates the spatial-temporal land usechanges with three processes scenario analysis of land usechange spatial regression analysis and spatial allocation ofland use changes The first process was carried out with thescenario analysis module which provides the data of totalland demands at the annual scale during a given period Byincluding the scenario analyses of land use changes a set ofspatially explicit simulation results of land use change can beexported by the DLS model [36] The total land use demandscan be set by several approaches such as trend analysismethods (eg linear interpolations or more sophisticatedeconometricmodels) and economicmodels In this study thetotal land demands during the simulation period were firstdetermined using trend analysis methods and then were usedto establish a scenario of land use change during 2006ndash2030

In the spatial regression module the relationships bet-ween land uses and influencing factors were analyzed viastepwise logistic regression analysis of past land use changesand their drivers [37] For each grid cell the total probabilityfor each land use type is calculated on basis of the multino-mial logistic regression at the pixel scale as follows

119875

119894119896=

1

1 + sum

119896 =119894

119890

119909119896120573119896

119894 = 119896

119890

119909119896120573119896

1 + sum

119896 =119894

119890

119909119896120573119896

119894 = 119896

(1)

where 119875119894119896

is the probability of conversion from land type 119894to 119896 in the cells under given driving factors 119883

1 119883

2 119883

119899

represent the driving factors of climate landform locationpopulation economic growth policy and other categories120573

1 120573

2sdot sdot sdot 120573

119899are the regression analysis coefficients of driving

factors for further estimation

In this study all the data of land use and the influencingfactors were prepared at the annual scale The land use datain 2000 and 2005 were used in the logistic regression Theassumed driving factors were categorized into five groupsclimate geophysics transportation location and socioeco-nomics The data of these factors in the corresponding yearswere also prepared (Table 2)

The spatial disaggregationmodule is used to spatially andexplicitly convert the land demands into land use changes atvarious locations of the study areaThe spatial disaggregationis carried out in an iterative procedure based on the proba-bility maps conversion rules historical land use maps andland demands under the scenarios The probability maps ofeach land use type were prepared with the logistic regressionresults Besides the rules of land use conversion were set foreach land use type whose value ranges from 0 to 1 A smallervalue means one land use type is more likely to be convertedto another type and vice versa The development-restrictedareas in the study area were also specified

24 Downscaling of GCM Climate Data GCMs are arguablythe best available tools formodeling future climate YetGCMsprovide information at a resolution that is too coarse tobe directly used in hydrological modeling [38] Thereforedownscaling is required to transform the low resolutionGCMoutputs to the high resolution climate features needed forhydrological simulationThe downscaling procedure is as fol-lows First the average annual precipitation and temperatureof 30 years (1980ndash2010) were calculated which were adoptedas a baseline for selecting the GCMs We considered futureclimate change scenarios for the basin (Figure 2) by usingthe spatially distributed outputs from 10GCMs under RCP45 scenario The climate projections of Max Planck Institute(MPI) were downscaled to the 3 km times 3 km grid in the studyarea and bias was corrected and climate change scenarioswere developed by MPI for Meteorology The annual meanvalues of 10GCM spatial data from 2006 to 2030 were cal-culated according to the basin perimeter and one out of tenGCMs was also selected based on the historical trends andannual averages of temperature and precipitation The MPI

Advances in Meteorology 5

Table 2 Results of logistical regression for the six land use types with 13 driving factors

Cultivated land Forest land Grassland Water Built-up area Unused land

Slope minus0000049 0000121 minus742119864 minus 05 minus776119864 minus 06 minus11119864 minus 06 00000105(minus2752)

lowastlowastlowast

(minus6365)

lowastlowastlowast

(minus1889)

lowastlowastlowast

(minus724)

lowastlowastlowast

(minus207)

lowast

(minus264)

lowastlowast

Elevation minus0000136 minus302E minus 05 0000116 minus00000149 minus59E minus 06 00000711(minus5382)lowastlowastlowast (minus1113)lowastlowastlowast (minus208)lowastlowastlowast (minus976)lowastlowastlowast (minus785)lowastlowastlowast (minus1256)

lowastlowastlowast

Rain minus0000045 00000228 00000419 00000136 14E minus 06 minus00000347(minus1609)

lowastlowastlowast

(minus761)

lowastlowastlowast

(minus679)

lowastlowastlowast

(minus805)

lowastlowastlowast

minus17 (minus554)

lowastlowastlowast

Radiation minus0000407 0000243 minus0000923 minus864119864 minus 06 116119864 minus 05 000108(minus2427)

lowastlowastlowast

(minus1355)

lowastlowastlowast

(minus2498)

lowastlowastlowast (minus086) (minus234)

lowast

(minus2891)

lowastlowastlowast

gt0∘C accumulated temperature minus00000106 00000127 0000026 minus388E minus 06 minus72E minus 07 minus00000234(minus2403)

lowastlowastlowast

(minus2678)

lowastlowastlowast

(minus2659)

lowastlowastlowast

(minus1455)

lowastlowastlowast

(minus552)

lowastlowastlowast

(minus2367)

lowastlowastlowast

Soil organic matter 00685 0232 minus0186 00152 minus000655 minus0123(minus938)

lowastlowastlowast

(minus2964)

lowastlowastlowast

(minus1158)

lowastlowastlowast

(minus346)

lowastlowastlowast

(minus303)

lowastlowast

(minus752)

lowastlowastlowast

pH value 000769 minus00131 minus000541 000171 0000719 000834(minus715)

lowastlowastlowast

(minus1132)

lowastlowastlowast

(minus228)

lowast

(minus264)

lowastlowast

(minus226)

lowast

(minus347)

lowastlowastlowast

Population density 0000247 minus782119864 minus 05 minus316119864 minus 05 00000206 minus32119864 minus 06 minus0000154(minus3218)

lowastlowastlowast

(minus952)

lowastlowastlowast (minus187) (minus446)

lowastlowastlowast (minus140) (minus900)

lowastlowastlowast

Per capita GDP 0000548 minus308119864 minus 05 minus0000185 minus000002 0000411 minus0000723(minus7602)

lowastlowastlowast

(minus399)

lowastlowastlowast

(minus1162)

lowastlowastlowast

(minus461)

lowastlowastlowast

(minus19254)

lowastlowastlowast

(minus4487)

lowastlowastlowast

Distance to railway 0000193 0000229 0000979 000000653 172E minus 05 minus000142(minus1355)

lowastlowastlowast

(minus1496)

lowastlowastlowast

(minus3116)

lowastlowastlowast

minus076 (minus407)

lowastlowastlowast

(minus4471)

lowastlowastlowast

Distance to road minus0000221 minus0000335 000111 minus0000066 minus14119864 minus 05 minus0000477(minus1845)

lowastlowastlowast

(minus2606)

lowastlowastlowast

(minus4208)

lowastlowastlowast

(minus913)

lowastlowastlowast

(minus379)

lowastlowastlowast

(minus1779)

lowastlowastlowast

Distance to water source minus0000338 00000239 0000926 minus0000346 minus52119864 minus 06 minus0000261(minus1446)

lowastlowastlowast

minus095 (minus1796)

lowastlowastlowast

(minus2455)

lowastlowastlowast (minus075) (minus499)

lowastlowastlowast

Distance to city minus0000125 minus0000108 minus0000567 00000996 minus2119864 minus 05 000072(minus811)

lowastlowastlowast

(minus658)

lowastlowastlowast

(minus1676)

lowastlowastlowast

(minus1078)

lowastlowastlowast

(minus431)

lowastlowastlowast

(minus2098)

lowastlowastlowast

Note 119905 statistics in parentheses lowastsignificant at 10 lowastlowastsignificant at 5 lowastlowastlowastsignificant at 1

Observed

ACCESS

BCC

BNU

CanESM

CESM

FGOALS

MIROC

MPI

Fitted lineYear

Tem

pera

ture

(∘C)

35

40

45

50

55

60

65

70

75

80

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

(a)

ACCESS

BCCBNU

CanESM

CESMFGOALS

MIROC

MPILinear fit of B

Year

Prec

ipita

tion

(mm

yea

r)

500

450

400

350

300

250

200

150

100

Rain

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

(b)

Figure 2 The trends in (a) observed (grey line running average in black) and projected (10AR5 GCMs colored dash lines) temperature (b)observed and projected precipitation

6 Advances in Meteorology

Table 3 List of calibration parameters and the optimized values

Parameter Description Range Optimized valueTLAPS Temperature lapse rate [∘Ckm] 0 minus10 minus38PLAPS Precipitation lapse rate [mmH2Okm] 0 100 58SFTMP Snowfall temperature [∘C] minus2 +2 09SMTMP Snow melt base temperature [∘C] minus5 +5 21SNOEB Initial snow water content in elevation band [mm] 50 230 100TIMP Snowpack temperature lag factor 038ndash062 049SMFMN Melt factor for snow on December 21 [mmH2O

∘C-day] 305ndash351 325SMFMX Melt factor for snow on June 21 [mmH2O

∘C-day] 585ndash627 602SURLAG Surface runoff lag time [days] 418ndash519 468

model was finally chosen through comparison The result ofMPI model originates in MPI and the spatial resolution is1865∘ (LAT) times 1875∘ (LON) Then the parameters in MPIGCM were transformed into the forcing data of a regionalclimate model in weather research and forecasting (WRF)simulation and thereafter dynamical downscaling simulationwas performed at the spatial resolution of 3 km for the periodof 2006ndash2030We considered the impacts of land use changeson regional climate and the land cover data before WRFsimulation were dynamically replaced with land use changedata based on the simulation with the DLSmodel Finally thedata simulated by the regional climate model were matchedwith meteorological sites and the meteorological site datawere prepared for the simulation with SWAT

25 Simulation of Hydrological Cycle with the SWAT ModelThe study area was first divided into subwatersheds whichwere subdivided into hydrological response units (HRUs)Besides for each subwatershed the climate data used aretaken from the GCM grid point that is the closest to itscentroid To improve performance the SWAT model wascalibrated and validated by adjusting several parameters andcomparing the simulated streamflow with observed valuesThe most sensitive parameters were identified with the built-in sensitivity analysis tool in SWAT [39]The daily streamflowobservation data from Yingluoxia Hydrological Station in2004 were used for calibration and the observation data in2005 were used for validation It should be noted that thefirst three years were used as a warm-up period to mitigatethe effects of unknown initial conditions which were thenexcluded from the subsequent analysis The ability of theSWATmodel to replicate the temporal trends in the historicalhydrological observations was assessed using the coefficientof determination (1198772) the Nash and Sutcliffe (1970) modelefficiency (NSE) and the root mean square error (RMSE)

3 Result and Discussion

31 Calibration and Validation The SWAT model was cali-brated for 2004 and validated for 2005 using the daily stream-flow observation data from four gauging stations withinthe study area Finally fifteen parameters were selected forthe calibration (Table 2) which are associated with snow

(SFTMP SMTMP SMFMX SMFMN and TIMP) runoff(CN2) groundwater (ALPHA BF and GW DELAY) soil(SOL AWC) channel (CH N and CH K2) and evaporation(ESCO) processes After the sensitivity analysis 9 relativelymore sensitive parameters were identified for the calibrationMost of the parameters were adjusted based on multipletrials and the SWATmodel was calibrated using an automaticcalibration technique with the program Sequential Uncer-tainty Fitting Version (SUFI-2)With SUFI-2 sensitive initialand default parameters related to hydrology varied simulta-neously until an optimal solution was achieved The mostsensitive parameters with their best ranges and best-fittedvalues are shown in Table 3 Finally these best-fitted valueswere used to adjust the initial model inputs for the simulationduring 2006ndash2030 The model was validated using dailystreamflow observation data from the Yingluoxia Hydrologi-cal Station in 2005 The validation results show that the NSEis 078 and 1198772 of the observed and simulated data is 081(Figure 3) demonstrating the high behavioral performanceof the SWAT model

32 Future Climate under the RCP 45 Scenario Based on thedownscaled GCM climate data we calculated the mean tem-perature and precipitation of the 9 lattice points around thegrid that included Qilian meteorological station The resultswere compared with the mean monthly temperatures andprecipitation of the meteorological station during 1981ndash2005The monthly mean temperatures of the 25 years ranged fromminus3 to 3∘C and increased by around 08∘CThe mean monthlyprecipitation ranges from minus03 to 10 and increased byaround 78 (Figure 4) The increase range of mean monthlyprecipitation is large while the range of reduction is smaller

33 Future Land Use Change Simulated with DLS The resultssuggested the change in one land use type was influencedby multiple factors and the 13 driving factors can reason-ably explain the spatial patterns of all land use types Forexample the existence of forest land was significantly influ-enced by all the 13 driving factors while the existence ofcultivated land and grassland was affected by the altitudedistance and soil factors The future land uses for 2006ndash2030 were simulated with the DLS model by combining

Advances in Meteorology 7

Calibration in 2004

Sim

ulat

ion

(m3s

)

Measurement (m3s)

minus50

0 50 100 150 200 250 300 350

0

50

100

150

200

250

300

350

R2= 085

Ens = 082

(a)

Validation in 2005

Sim

ulat

ion

(m3s

)Measurement (m3

s)0 50 100 150 200 250 300

0

50

100

150

200

250

R2= 081

Ens = 078

(b)

Figure 3 The calibration and validation on streamflow of the SWAT

Trend curve

(∘C)

minus3

minus2

minus1

0

1

2

3

0 50 100 150 200 250 300

(a)

()

minus2

0

2

4

6

8

10

0 50 100 150 200 250 300

(b)

Figure 4 The difference of average monthly temperature and precipitation between 1981ndash2005 and 2006ndash2030 in Qilian Weather observedstation

the probability maps prepared with logistic regression anal-ysis the land demands under different scenarios and themap of development-restricted areas The simulation resultsindicated that the most dramatic land use changes during2006ndash2030 will mainly occur in the upper reach and some

parts of the middle reach of Heihe River Basin Comparedto 2005 the areas of forest land and unused land in 2030 willdecrease by 62 and 16 respectively while the areas ofbuilt-up land cultivated land and grassland will increase by17 13 and 48 respectively (Figure 5) The significant

8 Advances in Meteorology

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E 102

∘E

2000

39∘N

38∘N

39∘N

38∘N

40∘N

(a)

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E

2005

39∘N

38∘N

39∘N

38∘N

(b)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2010

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

38∘N

39∘N

40∘N

38∘N

(c)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2030

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

40∘N

38∘N

39∘N

38∘N

(d)

Figure 5 Comparison of the land uses interpreted in 2000 2005 and simulated in 2010 2030

increase of grassland area may mainly result from the steadypasture construction and this uptrend may continue in thefuture owing to the increasing demand for pasture products

34 Impacts of Climate and Land Use Changes on WatershedHydrology Four simulation experiments were designedbased on the land use data and climate data In the baselineexperiment for the period during 1981ndash2005 the wateryield was simulated with the land use data in 2000 2005and the weather station observations during 1981ndash2005(Figure 6(b)) Then three scenarios for the period during2006ndash2030 were designed based on the land use and climatechange (Figure 6(a)) the results from which were comparedwith that in the baseline experiment In the first scenarioduring 2006ndash2030 the water yield was simulation with theland use data in 2010 and 2030 temperature data during2006ndash2030 and the precipitation data during 1981ndash2005Thesimulation result shows that the impacts of future land use

change on the water yield vary with seasons and the landuse change will have negative overall influence on the wateryield with an influence degree of minus18 according to theannual mean water yield

The second scenario during 2006ndash2030 was based onscenarios of temperature and land use changes The secondexperiment used the land use data in 2010 and 2030 scenariodata of temperature during 2006ndash2030 and precipitation dataduring 1981ndash2005 The analysis of climate change scenariosshows that the average temperature will rise by 08∘C between1981ndash2005 and 2006ndash2030 The simulation result in thesecond experiment shows that the land use and temperaturechanges will make the water yield change by 06ndash11 thechange range of which is relatively smaller compared to thesimulation results under the scenario with only land usechange The reasons may be that the temperature rise andmelting of a small amount of snow slightly offset the adverseeffects of land use change At the same time the higher

Advances in Meteorology 9

Case ICase II

Case III

minus80

minus60

minus40

minus20

0

20

40

60

80

()

2006

12

1

2007

12

1

2008

12

1

2009

12

1

2010

12

1

2011

12

1

2012

12

1

2013

12

1

2014

12

1

2015

12

1

2016

12

1

2017

12

1

2018

12

1

2019

12

1

2020

12

1

2021

12

1

2022

12

1

2023

12

1

2024

12

1

2025

12

1

2026

12

1

2027

12

1

2028

12

1

(a)

1981

12

1

1982

12

1

1983

12

1

1984

12

1

1985

12

1

1986

12

1

1987

12

1

1988

12

1

1989

12

1

1990

12

1

1991

12

1

1992

12

1

1993

12

1

1994

12

1

1995

12

1

1996

12

1

1997

12

1

1998

12

1

1999

12

1

2000

12

1

2001

12

1

2002

12

1

2003

12

1

(mm

)

Simulated water yield during 1981ndash2005

40

35

30

25

20

15

10

5

0

(b)

Figure 6 Comparison of water yields under three scenarios

temperatures will result in more winter precipitation in theform of rain rather than snow leading to the hydrologicconsequences including increased winter discharge a shiftin the spring snowmelt peak to earlier in the season anddecreased summer discharge

The third scenario during 2006ndash2030 involves scenariosof changes in all of land use temperature and precipitationThe land use data in 2010 and 2030 and temperature andprecipitation data during 2006ndash2030 were used under thethird scenario The simulation result shows that these threefactors jointly have positive impacts on the water yieldmaking the basin water yield increase by about 98 Theincrease of the basin water yield is mainly caused by thechange in precipitation which will increase by around 108during 2006ndash2030 in comparison to that during 1981ndash2005Overall the simulation results suggest that the basin wateryield will increase in the future under different scenarios ofclimate and land use changes

4 Discussion and Conclusions

In the Heihe River Basin the upper reaches are featuredwith the generation and use of blue water while the lowerreach and surrounding areas are characterized by naturalecosystems and a low population density LULC is defined assyndromes of human activities such as agriculture forestryand building construction and most of previous studies onlyfocused on the hydrological influence of LULC change in theupper reachThe separation between hydrological impacts ofland use and climate changes has never been studied in theupper andmiddle reaches of the Heihe River Basin Howeverwe argue that studying the hydrological processes in theupper and middle reaches is essential since water supply tothe lower reach is impacted by both the climate change andhuman activities in the upper and middle reaches

In this study we analyzed the impacts of potential climateand land use changes on the water yield in the upper and

10 Advances in Meteorology

middle reaches of Heihe River Basin based on the simulationwith the SWAT model The results show that the water yieldwas more affected by climate change than by land use changeThis indicates that the predicted increase in precipitation willexert more significant impacts on the watershed hydrologythan the predicted land use changes will However the anal-ysis of the projected streamflow changes shows that there arehigher uncertainties in the dry season compared with thewet season in the simulation with the hydrological modeland GCMs climate data It is difficult to accurately projectthe hydrological changes since there are various uncertaintiesassociated with the future Green House Gas (GHG) emissionscenarios GCM structure downscaling method LULC andhydrological models In particular water resource managersare generally confronted with complex problems in sustain-able management and conservation of water resources dueto the uncertainties in the future hydrological projectionunder climate and land use changes It is therefore crucial toconsider both land use and climate changes in water resourceplanning for the Heihe River Basin so as to mitigate theirnegative hydrological impacts and more valuable informa-tion may be provided to the water resource managers if theseuncertainties in the future hydrological projection can beeffectively reduced through advancedmodeling and research

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was financially supported by themajor researchplan of the National Natural Science Foundation of China(Grant no 91325302) National Basic Research Program ofChina (973 Program) (no 2010CB950904) and the NationalNatural Science Funds of China for Distinguished YoungScholar (Grant no 71225005)

References

[1] Z ZaludM TrnkaMDubrovsky P Hlavinka D Semeradovaand E Kocmankova ldquoClimate change impacts on selectedaspects of the Czech agricultural productionrdquo Plant ProtectionScience vol 45 pp S11ndashS19 2009

[2] E Lorencova J Frelichova E Nelson and D Vackar ldquoPast andfuture impacts of land use and climate change on agriculturalecosystem services in the Czech Republicrdquo Land Use Policy vol33 pp 183ndash194 2013

[3] RMahmood A I Quintanar G Conner et al ldquoImpacts of landuseland cover change on climate and future research prioritiesrdquoBulletin of the AmericanMeteorological Society vol 91 no 1 pp37ndash46 2010

[4] Z Ma S Kang L Zhang L Tong and X Su ldquoAnalysis ofimpacts of climate variability and human activity on streamflowfor a river basin in arid region of northwest Chinardquo Journal ofHydrology vol 352 no 3-4 pp 239ndash249 2008

[5] B R Scanlon I Jolly M Sophocleous and L Zhang ldquoGlobalimpacts of conversions from natural to agricultural ecosystems

on water resources quantity versus qualityrdquo Water ResourcesResearch vol 43 no 3 Article ID W03437 2007

[6] L M Mango A M Melesse M E McClain D Gann and S GSetegn ldquoLand use and climate change impacts on the hydrologyof the upper Mara River Basin Kenya results of a modelingstudy to support better resource managementrdquo Hydrology andEarth System Sciences vol 15 no 7 pp 2245ndash2258 2011

[7] C J Vorosmarty P Green J Salisbury and R B LammersldquoGlobal water resources vulnerability from climate change andpopulation growthrdquo Science vol 289 no 5477 pp 284ndash2882000

[8] S Qi G Sun Y Wang S G McNulty and J A M MyersldquoStreamflow response to climate and landuse changes in acoastal watershed in North Carolinardquo Transactions of theASABE vol 52 no 3 pp 739ndash749 2009

[9] J Kim J Choi C Choi and S Park ldquoImpacts of changes inclimate and land useland cover under IPCC RCP scenarios onstreamflow in theHoeya River Basin Koreardquo Science of the TotalEnvironment vol 452-453 pp 181ndash195 2013

[10] Z Li W-Z Liu X-C Zhang and F-L Zheng ldquoImpacts ofland use change and climate variability on hydrology in anagricultural catchment on the Loess Plateau of Chinardquo Journalof Hydrology vol 377 no 1-2 pp 35ndash42 2009

[11] R W Skaggs D M Amatya G Chescheir C Blanton andJ Gilliam ldquoEffect of drainage and management practices onhydrology of pine plantationrdquo in Proceedings of the InternationalConference onHydrology andManagement of ForestedWetlands2006

[12] T D Prowse S Beltaos J T Gardner et al ldquoClimate changeflow regulation and land-use effects on the hydrology of thePeace-Athabasca-Slave system Findings from the NorthernRivers Ecosystem Initiativerdquo Environmental Monitoring andAssessment vol 113 no 1ndash3 pp 167ndash197 2006

[13] B Dixon and J Earls ldquoEffects of urbanization on streamflowusing SWAT with real and simulated meteorological datardquoApplied Geography vol 35 no 1-2 pp 174ndash190 2012

[14] E Lioubimtseva R Cole J M Adams and G KapustinldquoImpacts of climate and land-cover changes in arid lands ofCentral Asiardquo Journal of Arid Environments vol 62 no 2 pp285ndash308 2005

[15] Z Wang D L Ficklin Y Zhang and M Zhang ldquoImpact ofclimate change on streamflow in the arid Shiyang River Basinof northwest Chinardquo Hydrological Processes vol 26 no 18 pp2733ndash2744 2012

[16] D R DrsquoAgostino L G Trisorio N Lamaddalena and R RagabldquoAssessing the results of scenarios of climate and land usechanges on the hydrology of an Italian catchment modellingstudyrdquo Hydrological Processes vol 24 no 19 pp 2693ndash27042010

[17] V Mishra K A Cherkauer D Niyogi et al ldquoA regional scaleassessment of land useland cover and climatic changes onwater and energy cycle in the upper Midwest United StatesrdquoInternational Journal of Climatology vol 30 no 13 pp 2025ndash2044 2010

[18] J A Vano J A Foley C J Kucharik andM T Coe ldquoEvaluatingthe seasonal and interannual variations in water balance inNorthern Wisconsin using a land surface modelrdquo Journal ofGeophysical Research G Biogeosciences vol 111 no 2 2006

[19] D Mao and K A Cherkauer ldquoImpacts of land-use changeon hydrologic responses in the Great Lakes regionrdquo Journal ofHydrology vol 374 no 1-2 pp 71ndash82 2009

Advances in Meteorology 11

[20] J-Y Park M-J Park M-J H-K Joh et al ldquoAssessment ofMIROC32 hires climate and clue-s land use change impacts onwatershed hydrology using Swatrdquo Transactions of the ASABEvol 54 no 5 pp 1713ndash1724 2011

[21] S J Kim H J Kwon G A Park and M S Lee ldquoAssessmentof land-use impact on streamflow via a grid-based modellingapproach including paddy fieldsrdquoHydrological Processes vol 19no 19 pp 3801ndash3817 2005

[22] E Lu E S Takle and J Manoj ldquoThe relationships betweenclimatic and hydrological changes in the upperMississippi riverbasin a SWAT and multi-GCM studyrdquo Journal of Hydrometeo-rology vol 11 no 2 pp 437ndash451 2010

[23] L Liu Z Liu X Ren T Fischer and Y Xu ldquoHydrologicalimpacts of climate change in the Yellow River Basin for the 21stcentury using hydrological model and statistical downscalingmodelrdquo Quaternary International vol 244 no 2 pp 211ndash2202011

[24] S Fall D Niyogi A Gluhovsky R A Pielke E Kalnay andG Rochon ldquoImpacts of land use land cover on temperaturetrends over the continental United States assessment using theNorth American Regional Reanalysisrdquo International Journal ofClimatology vol 30 no 13 pp 1980ndash1993 2010

[25] H Guo Q Hu and T Jiang ldquoAnnual and seasonal streamflowresponses to climate and land-cover changes in the Poyang Lakebasin Chinardquo Journal of Hydrology vol 355 no 1ndash4 pp 106ndash122 2008

[26] L Tang D Yang H Hu and B Gao ldquoDetecting the effect ofland-use change on streamflow sediment and nutrient losses bydistributed hydrological simulationrdquo Journal of Hydrology vol409 no 1-2 pp 172ndash182 2011

[27] M D Tomer and K E Schilling ldquoA simple approach todistinguish land-use and climate-change effects on watershedhydrologyrdquo Journal of Hydrology vol 376 no 1-2 pp 24ndash332009

[28] P F Juckem R J Hunt M P Anderson and D M RobertsonldquoEffects of climate and landmanagement change on streamflowin the driftless area ofWisconsinrdquo Journal of Hydrology vol 355no 1ndash4 pp 123ndash130 2008

[29] Q Feng G D Cheng and K N Endo ldquoTowards sustainabledevelopment of the environmentally degraded River Heihebasin Chinardquo Hydrological Sciences Journal vol 46 no 5 pp647ndash658 2001

[30] G-D Cheng H-L Xiao Z-M Xu J-X Li and M-F LuldquoWater issue and its countermeasure in the inland river basinsofNorthwest Chinamdasha case study inHeiheRiver Basinrdquo Journalof Glaciology and Geocryology vol 28 no 3 pp 406ndash413 2006

[31] A Jarvis H I Reuter A Nelson and E Guevara Hole-FilledSeamless SRTM Data V3 International Centre for TropicalAgriculture (CIAT) 2006

[32] X Deng Q Jiang H Su and F Wu ldquoTrace forest conversionsin Northeast China with a 1-km area percentage data modelrdquoJournal of Applied Remote Sensing vol 4 no 1 Article ID041893 pp 1ndash13 2010

[33] X Deng H Su and J Zhan ldquoIntegration of multiple datasources to simulate the dynamics of land systemsrdquo Sensors vol8 no 2 pp 620ndash634 2008

[34] X Deng J Liu Y Lin and C Shi ldquoA framework for the landuse change dynamics model compatible with rcmsrdquo Advancesin Meteorology vol 2013 Article ID 658941 7 pages 2013

[35] X Deng C Zhao and H Yan ldquoSystematic modeling ofimpacts of land use and land cover changes on regional climate

a reviewrdquo Advances in Meteorology vol 2013 Article ID 31767811 pages 2013

[36] X Deng F Yin Y Lin Q Jin and R Qu ldquoEquilibrium analyseson structural changes of land uses in Jiangxi Provincerdquo Journalof Food Agriculture and Environment vol 10 no 1 pp 846ndash8522012

[37] X Deng Q Jiang J Zhan S He and Y Lin ldquoSimulation on thedynamics of forest area changes in Northeast Chinardquo Journal ofGeographical Sciences vol 20 no 4 pp 495ndash509 2010

[38] X Deng C Zhao Y Lin et al ldquoDownscaling the impactsof large-scale LUCC on surface temperature along with IPCCRCPs a global perspectiverdquoEnergies vol 7 no 4 pp 2720ndash27392014

[39] S Neitsch J Arnold J E A Kiniry R Srinivasan and JWilliams ldquoSoil and water assessment tool userrsquos manual version2000rdquo GSWRL Report 02-06 2002

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EarthquakesJournal of

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Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

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MineralogyInternational Journal of

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MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geological ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geology Advances in

2 Advances in Meteorology

especially in arid and semiarid regions where the climatechange may significantly affect the hydrological cycle [15]The effects of LULC and climate changes on the streamflowaremore evident in the arid and semiarid regions One typicalexample is the Heihe River Basin in Northwest China whichis characterized by limited water resources and special hydro-climatic and physiographic conditions [16] Understandingthe hydrological responses to potential climate change is veryimportant for developing sustainable water resource man-agement strategies in this region However the impacts ofurbanization and deforestation on the hydrological cycle inarid and semiarid regions have been rarely documented [17ndash19] Overall there is still very limited understanding of theseparate as well as combined impacts of LULC and climatechanges on regional water and energy cycles and thereforemore in-depth research is needed especially in the arid andsemiarid regions [17]

There are many studies about the hydrological impacts ofland use change or climate change at the basin scale andmostof them were conducted with a hydrological model based ona series of land use data extracted from satellite images [9 2021] For example the impacts of land use change scenariosin the Wutu watershed North Taiwan were assessed usingthe conversion of a land use model (CLUE-s) and a gener-alized watershed loading functions model [16] A Soil WaterAssessment Tool (SWAT) and multiple General CirculationModels (GCMs) were used to investigate the relationshipbetween climatic and hydrological changes in the UpperMississippi River [22] Most of these studies assume nochange in LULC [4 23] but the impacts of climate changeon hydrology vary among regions and should be investigatedwith regional climate change scenarios [4] Besides thehydrological impacts of LULC change also vary with theclimatic conditions [9] For example water balance variablesmight add or subtract the impacts of climate change undervarying land cover conditions In particular the regionalLULC change can offset or magnify the changes in globalaverage temperature and can significantly alter the impactsassociated with global warming [17 24] In addition somestudies about the combined effects of climate and LULCchanges on streamflow showed that climate change wasgenerally more significant than LULC change in determiningthe basin hydrological response [25ndash27] For example climatedominates the changing streamflow in the Xinjiang RiverBasin of Poyang Lake China [25] However the hydrologicalcycle in a basin is a complex process influenced by climate andthe physical properties of the catchment and human activitiestogether [4 5] The complexity of these factors complicatesthe separation between effects of land use and climaticvariability on streamflow [26 28] Therefore it is still a chal-lenge to distinguish the effects of LULC change from that ofconcurrent climate variability [14]

This study aims to separate the impacts of climate andLULC changes on the hydrological cycle in the Heihe RiverBasin under future scenarios to provide some useful referenceinformation that can be used to improve the water resourcemanagement and guarantee the sustainable developmentThe climatic data predicted by General Circulation Models(GCMs) under RCP 45 scenario were used to represent

the climate change scenarios for 2006ndash2030 and the land usedata simulated with the Dynamic Land Use System (DLS)model were used to represent the land use change scenariosThe future hydrological cycle was simulated with the SWATbased on the scenario data of climate change and land usechange the hydrological impacts of which were analyzed bycomparing the simulation results under different scenariosThe results of this study can provide valuable information forguiding futurewater resourcemanagement in theHeiheRiverBasin as well as other arid and semiarid regions in China

2 Methods

21 Study Area The Heihe River Basin is the second largestinland river basin in China which lies between 37∘431015840ndash42∘411015840N and 97∘231015840ndash102∘721015840E with a total area of 12796 thou-sand km2 This basin expands across Qinghai ProvinceGansu Province and Inner Mongolia Autonomous Regionin Northwest China (Figure 1) With a total length of 821 kmthe Heihe River is divided into the upper middle and lowerreaches where the natural and socioeconomic characteristicsdiffer significantly For example the average annual precipita-tion is between 200 and 500mm less than 200mm and lessthan 50mm in these reaches respectively while the annualevaporation ranges from 700mm in the upper reach to morethan 3000mm in the lower reach [29] Besides the annualaverage temperature is 94∘C over the last 30 years and thisbasin enjoys a dry continental climate The altitude rangesfrom 869 to 5542m with an average of 1778mThemain landcover types are desert (5715 of total basin area) mountains(3316) and oasis (819) [30] The ecosystems from theupper reach to the lower reach are linked by the hydrologicalcycle but the hydrological cycle has significantly changeddue to the land use change and climate change in the pastdecades For example about 65 of the irrigation water inthe middle reach was extracted from the river runoff whichgreatly influences the hydrological cycle of the whole basinTherefore a detailed and integrated simulation analysis of thewater resources is critical and urgent for better water resourcemanagement in the Heihe River Basin

22 Data for Model Simulation The spatial data (ie topog-raphy soil and land use) historical climate data and hydro-logical data for the watershed were first prepared for theSWAT model and DLS model The topography was repre-sented with the 90m resolution digital elevation model(DEM) of Shuttle Radar TopographyMission (SRTM) (httpsrtmcsicgiarorg) [31] The soil data including texturedepth and drainage attributes were from the HarmonizedWorld Soil Database (HWSD) supplied by the Environmentaland Ecological Science Data Center for West China (West-DC) (httpwestdcwestgisaccn) The historical land usedata including 25 land use types which were derived fromLandsat TMETM images were provided by Data Centerof Chinese Academy of Sciences (CAS) [32] In particularthe glacier data were obtained from WestDC (httpwestdcwestgisaccn) and land use properties were directly obtain-ed from the SWAT model database (Table 1) The historicalhydrological data for SWATmodel calibration and validation

Advances in Meteorology 3

1340 5293

(km)

Yingluoxia

Qilian

Zhengyixia

Zhangye

Elevation (m)

99∘E 100

∘E 101∘E

99∘E 100

∘E 101∘E

40∘N

39∘N

38∘N

40∘N

39∘N

38∘N

100∘E90

∘E80∘E70

∘E ∘E110∘E120

100∘E90

∘E80∘E

∘E110 ∘E120

∘E130∘E140

50∘N

40∘N

30∘N

20∘N

50∘N

40∘N

30∘N

20∘N

10 20 30 400

Tibet

Xinjiang

Qinghai

Inner Mongolia

Gansu

Sichuan

Yunnan

Hunan

Jilin

Heilongjiang

Hebei

Hubei

HenanShaanxi

Shanxi

Guizhou

JiangxiFujian

Guangdong

Shandong

Liaoning

Jiangsu

ZhejiangChongqing

Ningxia

Taiwan

BeijingTianjin

ShanghaiAnhui

Hainan

Guangxi

(a)

Gray meadow soilGray desert soilSierozemFelty soilsMeadow SolonchakAeolian soil

Soil types

99∘E 100

∘E 101∘E

99∘E 100

∘E 101∘E

39∘N

38∘N

39∘N

38∘N

Cold desert soilChestnut soilBrown calcic soilIrrigated desert soil

Gray desert soilGray-brown desert soil

Chernozem

(b)

Yingluoxia

Qilian

Zhengyixia

Zhangye

Slope

30 30015(km)

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E 102

∘E

39∘N

38∘N

39∘N

38∘N

0ndash22ndash55ndash15

15ndash2020ndash25gt25

(c)

Sunan

Qilian

Gaotai

Shandan

Linze

Minle

Ganzhou

SunanSuzhou

Sunan

Jinta

Forest landGrasslandWater

Built-up areaUnused landCultivated land

99∘E 100

∘E 101∘E

99∘E 100

∘E 101∘E

39∘N

38∘N

39∘N

38∘N

(d)

Figure 1 The upper and middle Heihe River Basin (a) digital elevation model (b) soil type (c) slope and (d) land use in 2010

include the river flow data from four hydrological stationsThe hydrological observation data including annual datain Heihe River Basin during 1980ndash2010 were obtained fromthe hydrological yearbook provided by WestDC The riverflow data used for the model calibration and validation wereprovided by the Data Center of CAS The historical dailyweather data during 1980ndash2010 were collected from ChinaMeteorological Administration (CMA) including daily

precipitation maximum and minimum temperatures solarradiation humidity wind speed and wind direction from 13weather stations in or near the Heihe River Basin

23 Simulation of Land Use Changes with the DLS ModelThe DLS model is a set of applications used to simulate thechanging process of land use system and is an effective tool forsimulation of spatial-temporal land use changes to assist land

4 Advances in Meteorology

Table 1 Data used and sources

Data type Data sources Scale Description

DEM Shuttle Radar Topography Mission(SRTM) 90m Elevation

Land use Data Center of Chinese Academy ofScience 1 100000 The classification system contains 25

categories

Soil WestDC (httpwestdcwestgisaccn) 1 1000000Some parameters were calculated using a(Soil-Plant-Atmosphere-Water) Field andPond Hydrology model

Weather China Meteorological Administration Daily 5 weather stationsHydrological observation Hydrological yearbook Daily 4 stations

River flow Data Center of Chinese Academy ofScience 1 250000 River network diversion

Glacier WestDC (httpwestdcwestgisaccn) 1 100000 The attributes include width length anddepth

management [33] The DLS model consists of three modulesa spatial regression module that identifies the relationshipsbetween land uses and the influencing factors a scenarioanalysis module of land use changes that determines the landdemands at the regional level a spatial disaggregationmodulethat allocates land use changes from a regional level to thedisaggregated grid cells [34 35]

The DLS model simulates the spatial-temporal land usechanges with three processes scenario analysis of land usechange spatial regression analysis and spatial allocation ofland use changes The first process was carried out with thescenario analysis module which provides the data of totalland demands at the annual scale during a given period Byincluding the scenario analyses of land use changes a set ofspatially explicit simulation results of land use change can beexported by the DLS model [36] The total land use demandscan be set by several approaches such as trend analysismethods (eg linear interpolations or more sophisticatedeconometricmodels) and economicmodels In this study thetotal land demands during the simulation period were firstdetermined using trend analysis methods and then were usedto establish a scenario of land use change during 2006ndash2030

In the spatial regression module the relationships bet-ween land uses and influencing factors were analyzed viastepwise logistic regression analysis of past land use changesand their drivers [37] For each grid cell the total probabilityfor each land use type is calculated on basis of the multino-mial logistic regression at the pixel scale as follows

119875

119894119896=

1

1 + sum

119896 =119894

119890

119909119896120573119896

119894 = 119896

119890

119909119896120573119896

1 + sum

119896 =119894

119890

119909119896120573119896

119894 = 119896

(1)

where 119875119894119896

is the probability of conversion from land type 119894to 119896 in the cells under given driving factors 119883

1 119883

2 119883

119899

represent the driving factors of climate landform locationpopulation economic growth policy and other categories120573

1 120573

2sdot sdot sdot 120573

119899are the regression analysis coefficients of driving

factors for further estimation

In this study all the data of land use and the influencingfactors were prepared at the annual scale The land use datain 2000 and 2005 were used in the logistic regression Theassumed driving factors were categorized into five groupsclimate geophysics transportation location and socioeco-nomics The data of these factors in the corresponding yearswere also prepared (Table 2)

The spatial disaggregationmodule is used to spatially andexplicitly convert the land demands into land use changes atvarious locations of the study areaThe spatial disaggregationis carried out in an iterative procedure based on the proba-bility maps conversion rules historical land use maps andland demands under the scenarios The probability maps ofeach land use type were prepared with the logistic regressionresults Besides the rules of land use conversion were set foreach land use type whose value ranges from 0 to 1 A smallervalue means one land use type is more likely to be convertedto another type and vice versa The development-restrictedareas in the study area were also specified

24 Downscaling of GCM Climate Data GCMs are arguablythe best available tools formodeling future climate YetGCMsprovide information at a resolution that is too coarse tobe directly used in hydrological modeling [38] Thereforedownscaling is required to transform the low resolutionGCMoutputs to the high resolution climate features needed forhydrological simulationThe downscaling procedure is as fol-lows First the average annual precipitation and temperatureof 30 years (1980ndash2010) were calculated which were adoptedas a baseline for selecting the GCMs We considered futureclimate change scenarios for the basin (Figure 2) by usingthe spatially distributed outputs from 10GCMs under RCP45 scenario The climate projections of Max Planck Institute(MPI) were downscaled to the 3 km times 3 km grid in the studyarea and bias was corrected and climate change scenarioswere developed by MPI for Meteorology The annual meanvalues of 10GCM spatial data from 2006 to 2030 were cal-culated according to the basin perimeter and one out of tenGCMs was also selected based on the historical trends andannual averages of temperature and precipitation The MPI

Advances in Meteorology 5

Table 2 Results of logistical regression for the six land use types with 13 driving factors

Cultivated land Forest land Grassland Water Built-up area Unused land

Slope minus0000049 0000121 minus742119864 minus 05 minus776119864 minus 06 minus11119864 minus 06 00000105(minus2752)

lowastlowastlowast

(minus6365)

lowastlowastlowast

(minus1889)

lowastlowastlowast

(minus724)

lowastlowastlowast

(minus207)

lowast

(minus264)

lowastlowast

Elevation minus0000136 minus302E minus 05 0000116 minus00000149 minus59E minus 06 00000711(minus5382)lowastlowastlowast (minus1113)lowastlowastlowast (minus208)lowastlowastlowast (minus976)lowastlowastlowast (minus785)lowastlowastlowast (minus1256)

lowastlowastlowast

Rain minus0000045 00000228 00000419 00000136 14E minus 06 minus00000347(minus1609)

lowastlowastlowast

(minus761)

lowastlowastlowast

(minus679)

lowastlowastlowast

(minus805)

lowastlowastlowast

minus17 (minus554)

lowastlowastlowast

Radiation minus0000407 0000243 minus0000923 minus864119864 minus 06 116119864 minus 05 000108(minus2427)

lowastlowastlowast

(minus1355)

lowastlowastlowast

(minus2498)

lowastlowastlowast (minus086) (minus234)

lowast

(minus2891)

lowastlowastlowast

gt0∘C accumulated temperature minus00000106 00000127 0000026 minus388E minus 06 minus72E minus 07 minus00000234(minus2403)

lowastlowastlowast

(minus2678)

lowastlowastlowast

(minus2659)

lowastlowastlowast

(minus1455)

lowastlowastlowast

(minus552)

lowastlowastlowast

(minus2367)

lowastlowastlowast

Soil organic matter 00685 0232 minus0186 00152 minus000655 minus0123(minus938)

lowastlowastlowast

(minus2964)

lowastlowastlowast

(minus1158)

lowastlowastlowast

(minus346)

lowastlowastlowast

(minus303)

lowastlowast

(minus752)

lowastlowastlowast

pH value 000769 minus00131 minus000541 000171 0000719 000834(minus715)

lowastlowastlowast

(minus1132)

lowastlowastlowast

(minus228)

lowast

(minus264)

lowastlowast

(minus226)

lowast

(minus347)

lowastlowastlowast

Population density 0000247 minus782119864 minus 05 minus316119864 minus 05 00000206 minus32119864 minus 06 minus0000154(minus3218)

lowastlowastlowast

(minus952)

lowastlowastlowast (minus187) (minus446)

lowastlowastlowast (minus140) (minus900)

lowastlowastlowast

Per capita GDP 0000548 minus308119864 minus 05 minus0000185 minus000002 0000411 minus0000723(minus7602)

lowastlowastlowast

(minus399)

lowastlowastlowast

(minus1162)

lowastlowastlowast

(minus461)

lowastlowastlowast

(minus19254)

lowastlowastlowast

(minus4487)

lowastlowastlowast

Distance to railway 0000193 0000229 0000979 000000653 172E minus 05 minus000142(minus1355)

lowastlowastlowast

(minus1496)

lowastlowastlowast

(minus3116)

lowastlowastlowast

minus076 (minus407)

lowastlowastlowast

(minus4471)

lowastlowastlowast

Distance to road minus0000221 minus0000335 000111 minus0000066 minus14119864 minus 05 minus0000477(minus1845)

lowastlowastlowast

(minus2606)

lowastlowastlowast

(minus4208)

lowastlowastlowast

(minus913)

lowastlowastlowast

(minus379)

lowastlowastlowast

(minus1779)

lowastlowastlowast

Distance to water source minus0000338 00000239 0000926 minus0000346 minus52119864 minus 06 minus0000261(minus1446)

lowastlowastlowast

minus095 (minus1796)

lowastlowastlowast

(minus2455)

lowastlowastlowast (minus075) (minus499)

lowastlowastlowast

Distance to city minus0000125 minus0000108 minus0000567 00000996 minus2119864 minus 05 000072(minus811)

lowastlowastlowast

(minus658)

lowastlowastlowast

(minus1676)

lowastlowastlowast

(minus1078)

lowastlowastlowast

(minus431)

lowastlowastlowast

(minus2098)

lowastlowastlowast

Note 119905 statistics in parentheses lowastsignificant at 10 lowastlowastsignificant at 5 lowastlowastlowastsignificant at 1

Observed

ACCESS

BCC

BNU

CanESM

CESM

FGOALS

MIROC

MPI

Fitted lineYear

Tem

pera

ture

(∘C)

35

40

45

50

55

60

65

70

75

80

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

(a)

ACCESS

BCCBNU

CanESM

CESMFGOALS

MIROC

MPILinear fit of B

Year

Prec

ipita

tion

(mm

yea

r)

500

450

400

350

300

250

200

150

100

Rain

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

(b)

Figure 2 The trends in (a) observed (grey line running average in black) and projected (10AR5 GCMs colored dash lines) temperature (b)observed and projected precipitation

6 Advances in Meteorology

Table 3 List of calibration parameters and the optimized values

Parameter Description Range Optimized valueTLAPS Temperature lapse rate [∘Ckm] 0 minus10 minus38PLAPS Precipitation lapse rate [mmH2Okm] 0 100 58SFTMP Snowfall temperature [∘C] minus2 +2 09SMTMP Snow melt base temperature [∘C] minus5 +5 21SNOEB Initial snow water content in elevation band [mm] 50 230 100TIMP Snowpack temperature lag factor 038ndash062 049SMFMN Melt factor for snow on December 21 [mmH2O

∘C-day] 305ndash351 325SMFMX Melt factor for snow on June 21 [mmH2O

∘C-day] 585ndash627 602SURLAG Surface runoff lag time [days] 418ndash519 468

model was finally chosen through comparison The result ofMPI model originates in MPI and the spatial resolution is1865∘ (LAT) times 1875∘ (LON) Then the parameters in MPIGCM were transformed into the forcing data of a regionalclimate model in weather research and forecasting (WRF)simulation and thereafter dynamical downscaling simulationwas performed at the spatial resolution of 3 km for the periodof 2006ndash2030We considered the impacts of land use changeson regional climate and the land cover data before WRFsimulation were dynamically replaced with land use changedata based on the simulation with the DLSmodel Finally thedata simulated by the regional climate model were matchedwith meteorological sites and the meteorological site datawere prepared for the simulation with SWAT

25 Simulation of Hydrological Cycle with the SWAT ModelThe study area was first divided into subwatersheds whichwere subdivided into hydrological response units (HRUs)Besides for each subwatershed the climate data used aretaken from the GCM grid point that is the closest to itscentroid To improve performance the SWAT model wascalibrated and validated by adjusting several parameters andcomparing the simulated streamflow with observed valuesThe most sensitive parameters were identified with the built-in sensitivity analysis tool in SWAT [39]The daily streamflowobservation data from Yingluoxia Hydrological Station in2004 were used for calibration and the observation data in2005 were used for validation It should be noted that thefirst three years were used as a warm-up period to mitigatethe effects of unknown initial conditions which were thenexcluded from the subsequent analysis The ability of theSWATmodel to replicate the temporal trends in the historicalhydrological observations was assessed using the coefficientof determination (1198772) the Nash and Sutcliffe (1970) modelefficiency (NSE) and the root mean square error (RMSE)

3 Result and Discussion

31 Calibration and Validation The SWAT model was cali-brated for 2004 and validated for 2005 using the daily stream-flow observation data from four gauging stations withinthe study area Finally fifteen parameters were selected forthe calibration (Table 2) which are associated with snow

(SFTMP SMTMP SMFMX SMFMN and TIMP) runoff(CN2) groundwater (ALPHA BF and GW DELAY) soil(SOL AWC) channel (CH N and CH K2) and evaporation(ESCO) processes After the sensitivity analysis 9 relativelymore sensitive parameters were identified for the calibrationMost of the parameters were adjusted based on multipletrials and the SWATmodel was calibrated using an automaticcalibration technique with the program Sequential Uncer-tainty Fitting Version (SUFI-2)With SUFI-2 sensitive initialand default parameters related to hydrology varied simulta-neously until an optimal solution was achieved The mostsensitive parameters with their best ranges and best-fittedvalues are shown in Table 3 Finally these best-fitted valueswere used to adjust the initial model inputs for the simulationduring 2006ndash2030 The model was validated using dailystreamflow observation data from the Yingluoxia Hydrologi-cal Station in 2005 The validation results show that the NSEis 078 and 1198772 of the observed and simulated data is 081(Figure 3) demonstrating the high behavioral performanceof the SWAT model

32 Future Climate under the RCP 45 Scenario Based on thedownscaled GCM climate data we calculated the mean tem-perature and precipitation of the 9 lattice points around thegrid that included Qilian meteorological station The resultswere compared with the mean monthly temperatures andprecipitation of the meteorological station during 1981ndash2005The monthly mean temperatures of the 25 years ranged fromminus3 to 3∘C and increased by around 08∘CThe mean monthlyprecipitation ranges from minus03 to 10 and increased byaround 78 (Figure 4) The increase range of mean monthlyprecipitation is large while the range of reduction is smaller

33 Future Land Use Change Simulated with DLS The resultssuggested the change in one land use type was influencedby multiple factors and the 13 driving factors can reason-ably explain the spatial patterns of all land use types Forexample the existence of forest land was significantly influ-enced by all the 13 driving factors while the existence ofcultivated land and grassland was affected by the altitudedistance and soil factors The future land uses for 2006ndash2030 were simulated with the DLS model by combining

Advances in Meteorology 7

Calibration in 2004

Sim

ulat

ion

(m3s

)

Measurement (m3s)

minus50

0 50 100 150 200 250 300 350

0

50

100

150

200

250

300

350

R2= 085

Ens = 082

(a)

Validation in 2005

Sim

ulat

ion

(m3s

)Measurement (m3

s)0 50 100 150 200 250 300

0

50

100

150

200

250

R2= 081

Ens = 078

(b)

Figure 3 The calibration and validation on streamflow of the SWAT

Trend curve

(∘C)

minus3

minus2

minus1

0

1

2

3

0 50 100 150 200 250 300

(a)

()

minus2

0

2

4

6

8

10

0 50 100 150 200 250 300

(b)

Figure 4 The difference of average monthly temperature and precipitation between 1981ndash2005 and 2006ndash2030 in Qilian Weather observedstation

the probability maps prepared with logistic regression anal-ysis the land demands under different scenarios and themap of development-restricted areas The simulation resultsindicated that the most dramatic land use changes during2006ndash2030 will mainly occur in the upper reach and some

parts of the middle reach of Heihe River Basin Comparedto 2005 the areas of forest land and unused land in 2030 willdecrease by 62 and 16 respectively while the areas ofbuilt-up land cultivated land and grassland will increase by17 13 and 48 respectively (Figure 5) The significant

8 Advances in Meteorology

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E 102

∘E

2000

39∘N

38∘N

39∘N

38∘N

40∘N

(a)

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E

2005

39∘N

38∘N

39∘N

38∘N

(b)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2010

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

38∘N

39∘N

40∘N

38∘N

(c)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2030

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

40∘N

38∘N

39∘N

38∘N

(d)

Figure 5 Comparison of the land uses interpreted in 2000 2005 and simulated in 2010 2030

increase of grassland area may mainly result from the steadypasture construction and this uptrend may continue in thefuture owing to the increasing demand for pasture products

34 Impacts of Climate and Land Use Changes on WatershedHydrology Four simulation experiments were designedbased on the land use data and climate data In the baselineexperiment for the period during 1981ndash2005 the wateryield was simulated with the land use data in 2000 2005and the weather station observations during 1981ndash2005(Figure 6(b)) Then three scenarios for the period during2006ndash2030 were designed based on the land use and climatechange (Figure 6(a)) the results from which were comparedwith that in the baseline experiment In the first scenarioduring 2006ndash2030 the water yield was simulation with theland use data in 2010 and 2030 temperature data during2006ndash2030 and the precipitation data during 1981ndash2005Thesimulation result shows that the impacts of future land use

change on the water yield vary with seasons and the landuse change will have negative overall influence on the wateryield with an influence degree of minus18 according to theannual mean water yield

The second scenario during 2006ndash2030 was based onscenarios of temperature and land use changes The secondexperiment used the land use data in 2010 and 2030 scenariodata of temperature during 2006ndash2030 and precipitation dataduring 1981ndash2005 The analysis of climate change scenariosshows that the average temperature will rise by 08∘C between1981ndash2005 and 2006ndash2030 The simulation result in thesecond experiment shows that the land use and temperaturechanges will make the water yield change by 06ndash11 thechange range of which is relatively smaller compared to thesimulation results under the scenario with only land usechange The reasons may be that the temperature rise andmelting of a small amount of snow slightly offset the adverseeffects of land use change At the same time the higher

Advances in Meteorology 9

Case ICase II

Case III

minus80

minus60

minus40

minus20

0

20

40

60

80

()

2006

12

1

2007

12

1

2008

12

1

2009

12

1

2010

12

1

2011

12

1

2012

12

1

2013

12

1

2014

12

1

2015

12

1

2016

12

1

2017

12

1

2018

12

1

2019

12

1

2020

12

1

2021

12

1

2022

12

1

2023

12

1

2024

12

1

2025

12

1

2026

12

1

2027

12

1

2028

12

1

(a)

1981

12

1

1982

12

1

1983

12

1

1984

12

1

1985

12

1

1986

12

1

1987

12

1

1988

12

1

1989

12

1

1990

12

1

1991

12

1

1992

12

1

1993

12

1

1994

12

1

1995

12

1

1996

12

1

1997

12

1

1998

12

1

1999

12

1

2000

12

1

2001

12

1

2002

12

1

2003

12

1

(mm

)

Simulated water yield during 1981ndash2005

40

35

30

25

20

15

10

5

0

(b)

Figure 6 Comparison of water yields under three scenarios

temperatures will result in more winter precipitation in theform of rain rather than snow leading to the hydrologicconsequences including increased winter discharge a shiftin the spring snowmelt peak to earlier in the season anddecreased summer discharge

The third scenario during 2006ndash2030 involves scenariosof changes in all of land use temperature and precipitationThe land use data in 2010 and 2030 and temperature andprecipitation data during 2006ndash2030 were used under thethird scenario The simulation result shows that these threefactors jointly have positive impacts on the water yieldmaking the basin water yield increase by about 98 Theincrease of the basin water yield is mainly caused by thechange in precipitation which will increase by around 108during 2006ndash2030 in comparison to that during 1981ndash2005Overall the simulation results suggest that the basin wateryield will increase in the future under different scenarios ofclimate and land use changes

4 Discussion and Conclusions

In the Heihe River Basin the upper reaches are featuredwith the generation and use of blue water while the lowerreach and surrounding areas are characterized by naturalecosystems and a low population density LULC is defined assyndromes of human activities such as agriculture forestryand building construction and most of previous studies onlyfocused on the hydrological influence of LULC change in theupper reachThe separation between hydrological impacts ofland use and climate changes has never been studied in theupper andmiddle reaches of the Heihe River Basin Howeverwe argue that studying the hydrological processes in theupper and middle reaches is essential since water supply tothe lower reach is impacted by both the climate change andhuman activities in the upper and middle reaches

In this study we analyzed the impacts of potential climateand land use changes on the water yield in the upper and

10 Advances in Meteorology

middle reaches of Heihe River Basin based on the simulationwith the SWAT model The results show that the water yieldwas more affected by climate change than by land use changeThis indicates that the predicted increase in precipitation willexert more significant impacts on the watershed hydrologythan the predicted land use changes will However the anal-ysis of the projected streamflow changes shows that there arehigher uncertainties in the dry season compared with thewet season in the simulation with the hydrological modeland GCMs climate data It is difficult to accurately projectthe hydrological changes since there are various uncertaintiesassociated with the future Green House Gas (GHG) emissionscenarios GCM structure downscaling method LULC andhydrological models In particular water resource managersare generally confronted with complex problems in sustain-able management and conservation of water resources dueto the uncertainties in the future hydrological projectionunder climate and land use changes It is therefore crucial toconsider both land use and climate changes in water resourceplanning for the Heihe River Basin so as to mitigate theirnegative hydrological impacts and more valuable informa-tion may be provided to the water resource managers if theseuncertainties in the future hydrological projection can beeffectively reduced through advancedmodeling and research

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was financially supported by themajor researchplan of the National Natural Science Foundation of China(Grant no 91325302) National Basic Research Program ofChina (973 Program) (no 2010CB950904) and the NationalNatural Science Funds of China for Distinguished YoungScholar (Grant no 71225005)

References

[1] Z ZaludM TrnkaMDubrovsky P Hlavinka D Semeradovaand E Kocmankova ldquoClimate change impacts on selectedaspects of the Czech agricultural productionrdquo Plant ProtectionScience vol 45 pp S11ndashS19 2009

[2] E Lorencova J Frelichova E Nelson and D Vackar ldquoPast andfuture impacts of land use and climate change on agriculturalecosystem services in the Czech Republicrdquo Land Use Policy vol33 pp 183ndash194 2013

[3] RMahmood A I Quintanar G Conner et al ldquoImpacts of landuseland cover change on climate and future research prioritiesrdquoBulletin of the AmericanMeteorological Society vol 91 no 1 pp37ndash46 2010

[4] Z Ma S Kang L Zhang L Tong and X Su ldquoAnalysis ofimpacts of climate variability and human activity on streamflowfor a river basin in arid region of northwest Chinardquo Journal ofHydrology vol 352 no 3-4 pp 239ndash249 2008

[5] B R Scanlon I Jolly M Sophocleous and L Zhang ldquoGlobalimpacts of conversions from natural to agricultural ecosystems

on water resources quantity versus qualityrdquo Water ResourcesResearch vol 43 no 3 Article ID W03437 2007

[6] L M Mango A M Melesse M E McClain D Gann and S GSetegn ldquoLand use and climate change impacts on the hydrologyof the upper Mara River Basin Kenya results of a modelingstudy to support better resource managementrdquo Hydrology andEarth System Sciences vol 15 no 7 pp 2245ndash2258 2011

[7] C J Vorosmarty P Green J Salisbury and R B LammersldquoGlobal water resources vulnerability from climate change andpopulation growthrdquo Science vol 289 no 5477 pp 284ndash2882000

[8] S Qi G Sun Y Wang S G McNulty and J A M MyersldquoStreamflow response to climate and landuse changes in acoastal watershed in North Carolinardquo Transactions of theASABE vol 52 no 3 pp 739ndash749 2009

[9] J Kim J Choi C Choi and S Park ldquoImpacts of changes inclimate and land useland cover under IPCC RCP scenarios onstreamflow in theHoeya River Basin Koreardquo Science of the TotalEnvironment vol 452-453 pp 181ndash195 2013

[10] Z Li W-Z Liu X-C Zhang and F-L Zheng ldquoImpacts ofland use change and climate variability on hydrology in anagricultural catchment on the Loess Plateau of Chinardquo Journalof Hydrology vol 377 no 1-2 pp 35ndash42 2009

[11] R W Skaggs D M Amatya G Chescheir C Blanton andJ Gilliam ldquoEffect of drainage and management practices onhydrology of pine plantationrdquo in Proceedings of the InternationalConference onHydrology andManagement of ForestedWetlands2006

[12] T D Prowse S Beltaos J T Gardner et al ldquoClimate changeflow regulation and land-use effects on the hydrology of thePeace-Athabasca-Slave system Findings from the NorthernRivers Ecosystem Initiativerdquo Environmental Monitoring andAssessment vol 113 no 1ndash3 pp 167ndash197 2006

[13] B Dixon and J Earls ldquoEffects of urbanization on streamflowusing SWAT with real and simulated meteorological datardquoApplied Geography vol 35 no 1-2 pp 174ndash190 2012

[14] E Lioubimtseva R Cole J M Adams and G KapustinldquoImpacts of climate and land-cover changes in arid lands ofCentral Asiardquo Journal of Arid Environments vol 62 no 2 pp285ndash308 2005

[15] Z Wang D L Ficklin Y Zhang and M Zhang ldquoImpact ofclimate change on streamflow in the arid Shiyang River Basinof northwest Chinardquo Hydrological Processes vol 26 no 18 pp2733ndash2744 2012

[16] D R DrsquoAgostino L G Trisorio N Lamaddalena and R RagabldquoAssessing the results of scenarios of climate and land usechanges on the hydrology of an Italian catchment modellingstudyrdquo Hydrological Processes vol 24 no 19 pp 2693ndash27042010

[17] V Mishra K A Cherkauer D Niyogi et al ldquoA regional scaleassessment of land useland cover and climatic changes onwater and energy cycle in the upper Midwest United StatesrdquoInternational Journal of Climatology vol 30 no 13 pp 2025ndash2044 2010

[18] J A Vano J A Foley C J Kucharik andM T Coe ldquoEvaluatingthe seasonal and interannual variations in water balance inNorthern Wisconsin using a land surface modelrdquo Journal ofGeophysical Research G Biogeosciences vol 111 no 2 2006

[19] D Mao and K A Cherkauer ldquoImpacts of land-use changeon hydrologic responses in the Great Lakes regionrdquo Journal ofHydrology vol 374 no 1-2 pp 71ndash82 2009

Advances in Meteorology 11

[20] J-Y Park M-J Park M-J H-K Joh et al ldquoAssessment ofMIROC32 hires climate and clue-s land use change impacts onwatershed hydrology using Swatrdquo Transactions of the ASABEvol 54 no 5 pp 1713ndash1724 2011

[21] S J Kim H J Kwon G A Park and M S Lee ldquoAssessmentof land-use impact on streamflow via a grid-based modellingapproach including paddy fieldsrdquoHydrological Processes vol 19no 19 pp 3801ndash3817 2005

[22] E Lu E S Takle and J Manoj ldquoThe relationships betweenclimatic and hydrological changes in the upperMississippi riverbasin a SWAT and multi-GCM studyrdquo Journal of Hydrometeo-rology vol 11 no 2 pp 437ndash451 2010

[23] L Liu Z Liu X Ren T Fischer and Y Xu ldquoHydrologicalimpacts of climate change in the Yellow River Basin for the 21stcentury using hydrological model and statistical downscalingmodelrdquo Quaternary International vol 244 no 2 pp 211ndash2202011

[24] S Fall D Niyogi A Gluhovsky R A Pielke E Kalnay andG Rochon ldquoImpacts of land use land cover on temperaturetrends over the continental United States assessment using theNorth American Regional Reanalysisrdquo International Journal ofClimatology vol 30 no 13 pp 1980ndash1993 2010

[25] H Guo Q Hu and T Jiang ldquoAnnual and seasonal streamflowresponses to climate and land-cover changes in the Poyang Lakebasin Chinardquo Journal of Hydrology vol 355 no 1ndash4 pp 106ndash122 2008

[26] L Tang D Yang H Hu and B Gao ldquoDetecting the effect ofland-use change on streamflow sediment and nutrient losses bydistributed hydrological simulationrdquo Journal of Hydrology vol409 no 1-2 pp 172ndash182 2011

[27] M D Tomer and K E Schilling ldquoA simple approach todistinguish land-use and climate-change effects on watershedhydrologyrdquo Journal of Hydrology vol 376 no 1-2 pp 24ndash332009

[28] P F Juckem R J Hunt M P Anderson and D M RobertsonldquoEffects of climate and landmanagement change on streamflowin the driftless area ofWisconsinrdquo Journal of Hydrology vol 355no 1ndash4 pp 123ndash130 2008

[29] Q Feng G D Cheng and K N Endo ldquoTowards sustainabledevelopment of the environmentally degraded River Heihebasin Chinardquo Hydrological Sciences Journal vol 46 no 5 pp647ndash658 2001

[30] G-D Cheng H-L Xiao Z-M Xu J-X Li and M-F LuldquoWater issue and its countermeasure in the inland river basinsofNorthwest Chinamdasha case study inHeiheRiver Basinrdquo Journalof Glaciology and Geocryology vol 28 no 3 pp 406ndash413 2006

[31] A Jarvis H I Reuter A Nelson and E Guevara Hole-FilledSeamless SRTM Data V3 International Centre for TropicalAgriculture (CIAT) 2006

[32] X Deng Q Jiang H Su and F Wu ldquoTrace forest conversionsin Northeast China with a 1-km area percentage data modelrdquoJournal of Applied Remote Sensing vol 4 no 1 Article ID041893 pp 1ndash13 2010

[33] X Deng H Su and J Zhan ldquoIntegration of multiple datasources to simulate the dynamics of land systemsrdquo Sensors vol8 no 2 pp 620ndash634 2008

[34] X Deng J Liu Y Lin and C Shi ldquoA framework for the landuse change dynamics model compatible with rcmsrdquo Advancesin Meteorology vol 2013 Article ID 658941 7 pages 2013

[35] X Deng C Zhao and H Yan ldquoSystematic modeling ofimpacts of land use and land cover changes on regional climate

a reviewrdquo Advances in Meteorology vol 2013 Article ID 31767811 pages 2013

[36] X Deng F Yin Y Lin Q Jin and R Qu ldquoEquilibrium analyseson structural changes of land uses in Jiangxi Provincerdquo Journalof Food Agriculture and Environment vol 10 no 1 pp 846ndash8522012

[37] X Deng Q Jiang J Zhan S He and Y Lin ldquoSimulation on thedynamics of forest area changes in Northeast Chinardquo Journal ofGeographical Sciences vol 20 no 4 pp 495ndash509 2010

[38] X Deng C Zhao Y Lin et al ldquoDownscaling the impactsof large-scale LUCC on surface temperature along with IPCCRCPs a global perspectiverdquoEnergies vol 7 no 4 pp 2720ndash27392014

[39] S Neitsch J Arnold J E A Kiniry R Srinivasan and JWilliams ldquoSoil and water assessment tool userrsquos manual version2000rdquo GSWRL Report 02-06 2002

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EarthquakesJournal of

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Applied ampEnvironmentalSoil Science

Volume 2014

Mining

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

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GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

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MineralogyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geological ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geology Advances in

Advances in Meteorology 3

1340 5293

(km)

Yingluoxia

Qilian

Zhengyixia

Zhangye

Elevation (m)

99∘E 100

∘E 101∘E

99∘E 100

∘E 101∘E

40∘N

39∘N

38∘N

40∘N

39∘N

38∘N

100∘E90

∘E80∘E70

∘E ∘E110∘E120

100∘E90

∘E80∘E

∘E110 ∘E120

∘E130∘E140

50∘N

40∘N

30∘N

20∘N

50∘N

40∘N

30∘N

20∘N

10 20 30 400

Tibet

Xinjiang

Qinghai

Inner Mongolia

Gansu

Sichuan

Yunnan

Hunan

Jilin

Heilongjiang

Hebei

Hubei

HenanShaanxi

Shanxi

Guizhou

JiangxiFujian

Guangdong

Shandong

Liaoning

Jiangsu

ZhejiangChongqing

Ningxia

Taiwan

BeijingTianjin

ShanghaiAnhui

Hainan

Guangxi

(a)

Gray meadow soilGray desert soilSierozemFelty soilsMeadow SolonchakAeolian soil

Soil types

99∘E 100

∘E 101∘E

99∘E 100

∘E 101∘E

39∘N

38∘N

39∘N

38∘N

Cold desert soilChestnut soilBrown calcic soilIrrigated desert soil

Gray desert soilGray-brown desert soil

Chernozem

(b)

Yingluoxia

Qilian

Zhengyixia

Zhangye

Slope

30 30015(km)

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E 102

∘E

39∘N

38∘N

39∘N

38∘N

0ndash22ndash55ndash15

15ndash2020ndash25gt25

(c)

Sunan

Qilian

Gaotai

Shandan

Linze

Minle

Ganzhou

SunanSuzhou

Sunan

Jinta

Forest landGrasslandWater

Built-up areaUnused landCultivated land

99∘E 100

∘E 101∘E

99∘E 100

∘E 101∘E

39∘N

38∘N

39∘N

38∘N

(d)

Figure 1 The upper and middle Heihe River Basin (a) digital elevation model (b) soil type (c) slope and (d) land use in 2010

include the river flow data from four hydrological stationsThe hydrological observation data including annual datain Heihe River Basin during 1980ndash2010 were obtained fromthe hydrological yearbook provided by WestDC The riverflow data used for the model calibration and validation wereprovided by the Data Center of CAS The historical dailyweather data during 1980ndash2010 were collected from ChinaMeteorological Administration (CMA) including daily

precipitation maximum and minimum temperatures solarradiation humidity wind speed and wind direction from 13weather stations in or near the Heihe River Basin

23 Simulation of Land Use Changes with the DLS ModelThe DLS model is a set of applications used to simulate thechanging process of land use system and is an effective tool forsimulation of spatial-temporal land use changes to assist land

4 Advances in Meteorology

Table 1 Data used and sources

Data type Data sources Scale Description

DEM Shuttle Radar Topography Mission(SRTM) 90m Elevation

Land use Data Center of Chinese Academy ofScience 1 100000 The classification system contains 25

categories

Soil WestDC (httpwestdcwestgisaccn) 1 1000000Some parameters were calculated using a(Soil-Plant-Atmosphere-Water) Field andPond Hydrology model

Weather China Meteorological Administration Daily 5 weather stationsHydrological observation Hydrological yearbook Daily 4 stations

River flow Data Center of Chinese Academy ofScience 1 250000 River network diversion

Glacier WestDC (httpwestdcwestgisaccn) 1 100000 The attributes include width length anddepth

management [33] The DLS model consists of three modulesa spatial regression module that identifies the relationshipsbetween land uses and the influencing factors a scenarioanalysis module of land use changes that determines the landdemands at the regional level a spatial disaggregationmodulethat allocates land use changes from a regional level to thedisaggregated grid cells [34 35]

The DLS model simulates the spatial-temporal land usechanges with three processes scenario analysis of land usechange spatial regression analysis and spatial allocation ofland use changes The first process was carried out with thescenario analysis module which provides the data of totalland demands at the annual scale during a given period Byincluding the scenario analyses of land use changes a set ofspatially explicit simulation results of land use change can beexported by the DLS model [36] The total land use demandscan be set by several approaches such as trend analysismethods (eg linear interpolations or more sophisticatedeconometricmodels) and economicmodels In this study thetotal land demands during the simulation period were firstdetermined using trend analysis methods and then were usedto establish a scenario of land use change during 2006ndash2030

In the spatial regression module the relationships bet-ween land uses and influencing factors were analyzed viastepwise logistic regression analysis of past land use changesand their drivers [37] For each grid cell the total probabilityfor each land use type is calculated on basis of the multino-mial logistic regression at the pixel scale as follows

119875

119894119896=

1

1 + sum

119896 =119894

119890

119909119896120573119896

119894 = 119896

119890

119909119896120573119896

1 + sum

119896 =119894

119890

119909119896120573119896

119894 = 119896

(1)

where 119875119894119896

is the probability of conversion from land type 119894to 119896 in the cells under given driving factors 119883

1 119883

2 119883

119899

represent the driving factors of climate landform locationpopulation economic growth policy and other categories120573

1 120573

2sdot sdot sdot 120573

119899are the regression analysis coefficients of driving

factors for further estimation

In this study all the data of land use and the influencingfactors were prepared at the annual scale The land use datain 2000 and 2005 were used in the logistic regression Theassumed driving factors were categorized into five groupsclimate geophysics transportation location and socioeco-nomics The data of these factors in the corresponding yearswere also prepared (Table 2)

The spatial disaggregationmodule is used to spatially andexplicitly convert the land demands into land use changes atvarious locations of the study areaThe spatial disaggregationis carried out in an iterative procedure based on the proba-bility maps conversion rules historical land use maps andland demands under the scenarios The probability maps ofeach land use type were prepared with the logistic regressionresults Besides the rules of land use conversion were set foreach land use type whose value ranges from 0 to 1 A smallervalue means one land use type is more likely to be convertedto another type and vice versa The development-restrictedareas in the study area were also specified

24 Downscaling of GCM Climate Data GCMs are arguablythe best available tools formodeling future climate YetGCMsprovide information at a resolution that is too coarse tobe directly used in hydrological modeling [38] Thereforedownscaling is required to transform the low resolutionGCMoutputs to the high resolution climate features needed forhydrological simulationThe downscaling procedure is as fol-lows First the average annual precipitation and temperatureof 30 years (1980ndash2010) were calculated which were adoptedas a baseline for selecting the GCMs We considered futureclimate change scenarios for the basin (Figure 2) by usingthe spatially distributed outputs from 10GCMs under RCP45 scenario The climate projections of Max Planck Institute(MPI) were downscaled to the 3 km times 3 km grid in the studyarea and bias was corrected and climate change scenarioswere developed by MPI for Meteorology The annual meanvalues of 10GCM spatial data from 2006 to 2030 were cal-culated according to the basin perimeter and one out of tenGCMs was also selected based on the historical trends andannual averages of temperature and precipitation The MPI

Advances in Meteorology 5

Table 2 Results of logistical regression for the six land use types with 13 driving factors

Cultivated land Forest land Grassland Water Built-up area Unused land

Slope minus0000049 0000121 minus742119864 minus 05 minus776119864 minus 06 minus11119864 minus 06 00000105(minus2752)

lowastlowastlowast

(minus6365)

lowastlowastlowast

(minus1889)

lowastlowastlowast

(minus724)

lowastlowastlowast

(minus207)

lowast

(minus264)

lowastlowast

Elevation minus0000136 minus302E minus 05 0000116 minus00000149 minus59E minus 06 00000711(minus5382)lowastlowastlowast (minus1113)lowastlowastlowast (minus208)lowastlowastlowast (minus976)lowastlowastlowast (minus785)lowastlowastlowast (minus1256)

lowastlowastlowast

Rain minus0000045 00000228 00000419 00000136 14E minus 06 minus00000347(minus1609)

lowastlowastlowast

(minus761)

lowastlowastlowast

(minus679)

lowastlowastlowast

(minus805)

lowastlowastlowast

minus17 (minus554)

lowastlowastlowast

Radiation minus0000407 0000243 minus0000923 minus864119864 minus 06 116119864 minus 05 000108(minus2427)

lowastlowastlowast

(minus1355)

lowastlowastlowast

(minus2498)

lowastlowastlowast (minus086) (minus234)

lowast

(minus2891)

lowastlowastlowast

gt0∘C accumulated temperature minus00000106 00000127 0000026 minus388E minus 06 minus72E minus 07 minus00000234(minus2403)

lowastlowastlowast

(minus2678)

lowastlowastlowast

(minus2659)

lowastlowastlowast

(minus1455)

lowastlowastlowast

(minus552)

lowastlowastlowast

(minus2367)

lowastlowastlowast

Soil organic matter 00685 0232 minus0186 00152 minus000655 minus0123(minus938)

lowastlowastlowast

(minus2964)

lowastlowastlowast

(minus1158)

lowastlowastlowast

(minus346)

lowastlowastlowast

(minus303)

lowastlowast

(minus752)

lowastlowastlowast

pH value 000769 minus00131 minus000541 000171 0000719 000834(minus715)

lowastlowastlowast

(minus1132)

lowastlowastlowast

(minus228)

lowast

(minus264)

lowastlowast

(minus226)

lowast

(minus347)

lowastlowastlowast

Population density 0000247 minus782119864 minus 05 minus316119864 minus 05 00000206 minus32119864 minus 06 minus0000154(minus3218)

lowastlowastlowast

(minus952)

lowastlowastlowast (minus187) (minus446)

lowastlowastlowast (minus140) (minus900)

lowastlowastlowast

Per capita GDP 0000548 minus308119864 minus 05 minus0000185 minus000002 0000411 minus0000723(minus7602)

lowastlowastlowast

(minus399)

lowastlowastlowast

(minus1162)

lowastlowastlowast

(minus461)

lowastlowastlowast

(minus19254)

lowastlowastlowast

(minus4487)

lowastlowastlowast

Distance to railway 0000193 0000229 0000979 000000653 172E minus 05 minus000142(minus1355)

lowastlowastlowast

(minus1496)

lowastlowastlowast

(minus3116)

lowastlowastlowast

minus076 (minus407)

lowastlowastlowast

(minus4471)

lowastlowastlowast

Distance to road minus0000221 minus0000335 000111 minus0000066 minus14119864 minus 05 minus0000477(minus1845)

lowastlowastlowast

(minus2606)

lowastlowastlowast

(minus4208)

lowastlowastlowast

(minus913)

lowastlowastlowast

(minus379)

lowastlowastlowast

(minus1779)

lowastlowastlowast

Distance to water source minus0000338 00000239 0000926 minus0000346 minus52119864 minus 06 minus0000261(minus1446)

lowastlowastlowast

minus095 (minus1796)

lowastlowastlowast

(minus2455)

lowastlowastlowast (minus075) (minus499)

lowastlowastlowast

Distance to city minus0000125 minus0000108 minus0000567 00000996 minus2119864 minus 05 000072(minus811)

lowastlowastlowast

(minus658)

lowastlowastlowast

(minus1676)

lowastlowastlowast

(minus1078)

lowastlowastlowast

(minus431)

lowastlowastlowast

(minus2098)

lowastlowastlowast

Note 119905 statistics in parentheses lowastsignificant at 10 lowastlowastsignificant at 5 lowastlowastlowastsignificant at 1

Observed

ACCESS

BCC

BNU

CanESM

CESM

FGOALS

MIROC

MPI

Fitted lineYear

Tem

pera

ture

(∘C)

35

40

45

50

55

60

65

70

75

80

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

(a)

ACCESS

BCCBNU

CanESM

CESMFGOALS

MIROC

MPILinear fit of B

Year

Prec

ipita

tion

(mm

yea

r)

500

450

400

350

300

250

200

150

100

Rain

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

(b)

Figure 2 The trends in (a) observed (grey line running average in black) and projected (10AR5 GCMs colored dash lines) temperature (b)observed and projected precipitation

6 Advances in Meteorology

Table 3 List of calibration parameters and the optimized values

Parameter Description Range Optimized valueTLAPS Temperature lapse rate [∘Ckm] 0 minus10 minus38PLAPS Precipitation lapse rate [mmH2Okm] 0 100 58SFTMP Snowfall temperature [∘C] minus2 +2 09SMTMP Snow melt base temperature [∘C] minus5 +5 21SNOEB Initial snow water content in elevation band [mm] 50 230 100TIMP Snowpack temperature lag factor 038ndash062 049SMFMN Melt factor for snow on December 21 [mmH2O

∘C-day] 305ndash351 325SMFMX Melt factor for snow on June 21 [mmH2O

∘C-day] 585ndash627 602SURLAG Surface runoff lag time [days] 418ndash519 468

model was finally chosen through comparison The result ofMPI model originates in MPI and the spatial resolution is1865∘ (LAT) times 1875∘ (LON) Then the parameters in MPIGCM were transformed into the forcing data of a regionalclimate model in weather research and forecasting (WRF)simulation and thereafter dynamical downscaling simulationwas performed at the spatial resolution of 3 km for the periodof 2006ndash2030We considered the impacts of land use changeson regional climate and the land cover data before WRFsimulation were dynamically replaced with land use changedata based on the simulation with the DLSmodel Finally thedata simulated by the regional climate model were matchedwith meteorological sites and the meteorological site datawere prepared for the simulation with SWAT

25 Simulation of Hydrological Cycle with the SWAT ModelThe study area was first divided into subwatersheds whichwere subdivided into hydrological response units (HRUs)Besides for each subwatershed the climate data used aretaken from the GCM grid point that is the closest to itscentroid To improve performance the SWAT model wascalibrated and validated by adjusting several parameters andcomparing the simulated streamflow with observed valuesThe most sensitive parameters were identified with the built-in sensitivity analysis tool in SWAT [39]The daily streamflowobservation data from Yingluoxia Hydrological Station in2004 were used for calibration and the observation data in2005 were used for validation It should be noted that thefirst three years were used as a warm-up period to mitigatethe effects of unknown initial conditions which were thenexcluded from the subsequent analysis The ability of theSWATmodel to replicate the temporal trends in the historicalhydrological observations was assessed using the coefficientof determination (1198772) the Nash and Sutcliffe (1970) modelefficiency (NSE) and the root mean square error (RMSE)

3 Result and Discussion

31 Calibration and Validation The SWAT model was cali-brated for 2004 and validated for 2005 using the daily stream-flow observation data from four gauging stations withinthe study area Finally fifteen parameters were selected forthe calibration (Table 2) which are associated with snow

(SFTMP SMTMP SMFMX SMFMN and TIMP) runoff(CN2) groundwater (ALPHA BF and GW DELAY) soil(SOL AWC) channel (CH N and CH K2) and evaporation(ESCO) processes After the sensitivity analysis 9 relativelymore sensitive parameters were identified for the calibrationMost of the parameters were adjusted based on multipletrials and the SWATmodel was calibrated using an automaticcalibration technique with the program Sequential Uncer-tainty Fitting Version (SUFI-2)With SUFI-2 sensitive initialand default parameters related to hydrology varied simulta-neously until an optimal solution was achieved The mostsensitive parameters with their best ranges and best-fittedvalues are shown in Table 3 Finally these best-fitted valueswere used to adjust the initial model inputs for the simulationduring 2006ndash2030 The model was validated using dailystreamflow observation data from the Yingluoxia Hydrologi-cal Station in 2005 The validation results show that the NSEis 078 and 1198772 of the observed and simulated data is 081(Figure 3) demonstrating the high behavioral performanceof the SWAT model

32 Future Climate under the RCP 45 Scenario Based on thedownscaled GCM climate data we calculated the mean tem-perature and precipitation of the 9 lattice points around thegrid that included Qilian meteorological station The resultswere compared with the mean monthly temperatures andprecipitation of the meteorological station during 1981ndash2005The monthly mean temperatures of the 25 years ranged fromminus3 to 3∘C and increased by around 08∘CThe mean monthlyprecipitation ranges from minus03 to 10 and increased byaround 78 (Figure 4) The increase range of mean monthlyprecipitation is large while the range of reduction is smaller

33 Future Land Use Change Simulated with DLS The resultssuggested the change in one land use type was influencedby multiple factors and the 13 driving factors can reason-ably explain the spatial patterns of all land use types Forexample the existence of forest land was significantly influ-enced by all the 13 driving factors while the existence ofcultivated land and grassland was affected by the altitudedistance and soil factors The future land uses for 2006ndash2030 were simulated with the DLS model by combining

Advances in Meteorology 7

Calibration in 2004

Sim

ulat

ion

(m3s

)

Measurement (m3s)

minus50

0 50 100 150 200 250 300 350

0

50

100

150

200

250

300

350

R2= 085

Ens = 082

(a)

Validation in 2005

Sim

ulat

ion

(m3s

)Measurement (m3

s)0 50 100 150 200 250 300

0

50

100

150

200

250

R2= 081

Ens = 078

(b)

Figure 3 The calibration and validation on streamflow of the SWAT

Trend curve

(∘C)

minus3

minus2

minus1

0

1

2

3

0 50 100 150 200 250 300

(a)

()

minus2

0

2

4

6

8

10

0 50 100 150 200 250 300

(b)

Figure 4 The difference of average monthly temperature and precipitation between 1981ndash2005 and 2006ndash2030 in Qilian Weather observedstation

the probability maps prepared with logistic regression anal-ysis the land demands under different scenarios and themap of development-restricted areas The simulation resultsindicated that the most dramatic land use changes during2006ndash2030 will mainly occur in the upper reach and some

parts of the middle reach of Heihe River Basin Comparedto 2005 the areas of forest land and unused land in 2030 willdecrease by 62 and 16 respectively while the areas ofbuilt-up land cultivated land and grassland will increase by17 13 and 48 respectively (Figure 5) The significant

8 Advances in Meteorology

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E 102

∘E

2000

39∘N

38∘N

39∘N

38∘N

40∘N

(a)

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E

2005

39∘N

38∘N

39∘N

38∘N

(b)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2010

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

38∘N

39∘N

40∘N

38∘N

(c)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2030

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

40∘N

38∘N

39∘N

38∘N

(d)

Figure 5 Comparison of the land uses interpreted in 2000 2005 and simulated in 2010 2030

increase of grassland area may mainly result from the steadypasture construction and this uptrend may continue in thefuture owing to the increasing demand for pasture products

34 Impacts of Climate and Land Use Changes on WatershedHydrology Four simulation experiments were designedbased on the land use data and climate data In the baselineexperiment for the period during 1981ndash2005 the wateryield was simulated with the land use data in 2000 2005and the weather station observations during 1981ndash2005(Figure 6(b)) Then three scenarios for the period during2006ndash2030 were designed based on the land use and climatechange (Figure 6(a)) the results from which were comparedwith that in the baseline experiment In the first scenarioduring 2006ndash2030 the water yield was simulation with theland use data in 2010 and 2030 temperature data during2006ndash2030 and the precipitation data during 1981ndash2005Thesimulation result shows that the impacts of future land use

change on the water yield vary with seasons and the landuse change will have negative overall influence on the wateryield with an influence degree of minus18 according to theannual mean water yield

The second scenario during 2006ndash2030 was based onscenarios of temperature and land use changes The secondexperiment used the land use data in 2010 and 2030 scenariodata of temperature during 2006ndash2030 and precipitation dataduring 1981ndash2005 The analysis of climate change scenariosshows that the average temperature will rise by 08∘C between1981ndash2005 and 2006ndash2030 The simulation result in thesecond experiment shows that the land use and temperaturechanges will make the water yield change by 06ndash11 thechange range of which is relatively smaller compared to thesimulation results under the scenario with only land usechange The reasons may be that the temperature rise andmelting of a small amount of snow slightly offset the adverseeffects of land use change At the same time the higher

Advances in Meteorology 9

Case ICase II

Case III

minus80

minus60

minus40

minus20

0

20

40

60

80

()

2006

12

1

2007

12

1

2008

12

1

2009

12

1

2010

12

1

2011

12

1

2012

12

1

2013

12

1

2014

12

1

2015

12

1

2016

12

1

2017

12

1

2018

12

1

2019

12

1

2020

12

1

2021

12

1

2022

12

1

2023

12

1

2024

12

1

2025

12

1

2026

12

1

2027

12

1

2028

12

1

(a)

1981

12

1

1982

12

1

1983

12

1

1984

12

1

1985

12

1

1986

12

1

1987

12

1

1988

12

1

1989

12

1

1990

12

1

1991

12

1

1992

12

1

1993

12

1

1994

12

1

1995

12

1

1996

12

1

1997

12

1

1998

12

1

1999

12

1

2000

12

1

2001

12

1

2002

12

1

2003

12

1

(mm

)

Simulated water yield during 1981ndash2005

40

35

30

25

20

15

10

5

0

(b)

Figure 6 Comparison of water yields under three scenarios

temperatures will result in more winter precipitation in theform of rain rather than snow leading to the hydrologicconsequences including increased winter discharge a shiftin the spring snowmelt peak to earlier in the season anddecreased summer discharge

The third scenario during 2006ndash2030 involves scenariosof changes in all of land use temperature and precipitationThe land use data in 2010 and 2030 and temperature andprecipitation data during 2006ndash2030 were used under thethird scenario The simulation result shows that these threefactors jointly have positive impacts on the water yieldmaking the basin water yield increase by about 98 Theincrease of the basin water yield is mainly caused by thechange in precipitation which will increase by around 108during 2006ndash2030 in comparison to that during 1981ndash2005Overall the simulation results suggest that the basin wateryield will increase in the future under different scenarios ofclimate and land use changes

4 Discussion and Conclusions

In the Heihe River Basin the upper reaches are featuredwith the generation and use of blue water while the lowerreach and surrounding areas are characterized by naturalecosystems and a low population density LULC is defined assyndromes of human activities such as agriculture forestryand building construction and most of previous studies onlyfocused on the hydrological influence of LULC change in theupper reachThe separation between hydrological impacts ofland use and climate changes has never been studied in theupper andmiddle reaches of the Heihe River Basin Howeverwe argue that studying the hydrological processes in theupper and middle reaches is essential since water supply tothe lower reach is impacted by both the climate change andhuman activities in the upper and middle reaches

In this study we analyzed the impacts of potential climateand land use changes on the water yield in the upper and

10 Advances in Meteorology

middle reaches of Heihe River Basin based on the simulationwith the SWAT model The results show that the water yieldwas more affected by climate change than by land use changeThis indicates that the predicted increase in precipitation willexert more significant impacts on the watershed hydrologythan the predicted land use changes will However the anal-ysis of the projected streamflow changes shows that there arehigher uncertainties in the dry season compared with thewet season in the simulation with the hydrological modeland GCMs climate data It is difficult to accurately projectthe hydrological changes since there are various uncertaintiesassociated with the future Green House Gas (GHG) emissionscenarios GCM structure downscaling method LULC andhydrological models In particular water resource managersare generally confronted with complex problems in sustain-able management and conservation of water resources dueto the uncertainties in the future hydrological projectionunder climate and land use changes It is therefore crucial toconsider both land use and climate changes in water resourceplanning for the Heihe River Basin so as to mitigate theirnegative hydrological impacts and more valuable informa-tion may be provided to the water resource managers if theseuncertainties in the future hydrological projection can beeffectively reduced through advancedmodeling and research

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was financially supported by themajor researchplan of the National Natural Science Foundation of China(Grant no 91325302) National Basic Research Program ofChina (973 Program) (no 2010CB950904) and the NationalNatural Science Funds of China for Distinguished YoungScholar (Grant no 71225005)

References

[1] Z ZaludM TrnkaMDubrovsky P Hlavinka D Semeradovaand E Kocmankova ldquoClimate change impacts on selectedaspects of the Czech agricultural productionrdquo Plant ProtectionScience vol 45 pp S11ndashS19 2009

[2] E Lorencova J Frelichova E Nelson and D Vackar ldquoPast andfuture impacts of land use and climate change on agriculturalecosystem services in the Czech Republicrdquo Land Use Policy vol33 pp 183ndash194 2013

[3] RMahmood A I Quintanar G Conner et al ldquoImpacts of landuseland cover change on climate and future research prioritiesrdquoBulletin of the AmericanMeteorological Society vol 91 no 1 pp37ndash46 2010

[4] Z Ma S Kang L Zhang L Tong and X Su ldquoAnalysis ofimpacts of climate variability and human activity on streamflowfor a river basin in arid region of northwest Chinardquo Journal ofHydrology vol 352 no 3-4 pp 239ndash249 2008

[5] B R Scanlon I Jolly M Sophocleous and L Zhang ldquoGlobalimpacts of conversions from natural to agricultural ecosystems

on water resources quantity versus qualityrdquo Water ResourcesResearch vol 43 no 3 Article ID W03437 2007

[6] L M Mango A M Melesse M E McClain D Gann and S GSetegn ldquoLand use and climate change impacts on the hydrologyof the upper Mara River Basin Kenya results of a modelingstudy to support better resource managementrdquo Hydrology andEarth System Sciences vol 15 no 7 pp 2245ndash2258 2011

[7] C J Vorosmarty P Green J Salisbury and R B LammersldquoGlobal water resources vulnerability from climate change andpopulation growthrdquo Science vol 289 no 5477 pp 284ndash2882000

[8] S Qi G Sun Y Wang S G McNulty and J A M MyersldquoStreamflow response to climate and landuse changes in acoastal watershed in North Carolinardquo Transactions of theASABE vol 52 no 3 pp 739ndash749 2009

[9] J Kim J Choi C Choi and S Park ldquoImpacts of changes inclimate and land useland cover under IPCC RCP scenarios onstreamflow in theHoeya River Basin Koreardquo Science of the TotalEnvironment vol 452-453 pp 181ndash195 2013

[10] Z Li W-Z Liu X-C Zhang and F-L Zheng ldquoImpacts ofland use change and climate variability on hydrology in anagricultural catchment on the Loess Plateau of Chinardquo Journalof Hydrology vol 377 no 1-2 pp 35ndash42 2009

[11] R W Skaggs D M Amatya G Chescheir C Blanton andJ Gilliam ldquoEffect of drainage and management practices onhydrology of pine plantationrdquo in Proceedings of the InternationalConference onHydrology andManagement of ForestedWetlands2006

[12] T D Prowse S Beltaos J T Gardner et al ldquoClimate changeflow regulation and land-use effects on the hydrology of thePeace-Athabasca-Slave system Findings from the NorthernRivers Ecosystem Initiativerdquo Environmental Monitoring andAssessment vol 113 no 1ndash3 pp 167ndash197 2006

[13] B Dixon and J Earls ldquoEffects of urbanization on streamflowusing SWAT with real and simulated meteorological datardquoApplied Geography vol 35 no 1-2 pp 174ndash190 2012

[14] E Lioubimtseva R Cole J M Adams and G KapustinldquoImpacts of climate and land-cover changes in arid lands ofCentral Asiardquo Journal of Arid Environments vol 62 no 2 pp285ndash308 2005

[15] Z Wang D L Ficklin Y Zhang and M Zhang ldquoImpact ofclimate change on streamflow in the arid Shiyang River Basinof northwest Chinardquo Hydrological Processes vol 26 no 18 pp2733ndash2744 2012

[16] D R DrsquoAgostino L G Trisorio N Lamaddalena and R RagabldquoAssessing the results of scenarios of climate and land usechanges on the hydrology of an Italian catchment modellingstudyrdquo Hydrological Processes vol 24 no 19 pp 2693ndash27042010

[17] V Mishra K A Cherkauer D Niyogi et al ldquoA regional scaleassessment of land useland cover and climatic changes onwater and energy cycle in the upper Midwest United StatesrdquoInternational Journal of Climatology vol 30 no 13 pp 2025ndash2044 2010

[18] J A Vano J A Foley C J Kucharik andM T Coe ldquoEvaluatingthe seasonal and interannual variations in water balance inNorthern Wisconsin using a land surface modelrdquo Journal ofGeophysical Research G Biogeosciences vol 111 no 2 2006

[19] D Mao and K A Cherkauer ldquoImpacts of land-use changeon hydrologic responses in the Great Lakes regionrdquo Journal ofHydrology vol 374 no 1-2 pp 71ndash82 2009

Advances in Meteorology 11

[20] J-Y Park M-J Park M-J H-K Joh et al ldquoAssessment ofMIROC32 hires climate and clue-s land use change impacts onwatershed hydrology using Swatrdquo Transactions of the ASABEvol 54 no 5 pp 1713ndash1724 2011

[21] S J Kim H J Kwon G A Park and M S Lee ldquoAssessmentof land-use impact on streamflow via a grid-based modellingapproach including paddy fieldsrdquoHydrological Processes vol 19no 19 pp 3801ndash3817 2005

[22] E Lu E S Takle and J Manoj ldquoThe relationships betweenclimatic and hydrological changes in the upperMississippi riverbasin a SWAT and multi-GCM studyrdquo Journal of Hydrometeo-rology vol 11 no 2 pp 437ndash451 2010

[23] L Liu Z Liu X Ren T Fischer and Y Xu ldquoHydrologicalimpacts of climate change in the Yellow River Basin for the 21stcentury using hydrological model and statistical downscalingmodelrdquo Quaternary International vol 244 no 2 pp 211ndash2202011

[24] S Fall D Niyogi A Gluhovsky R A Pielke E Kalnay andG Rochon ldquoImpacts of land use land cover on temperaturetrends over the continental United States assessment using theNorth American Regional Reanalysisrdquo International Journal ofClimatology vol 30 no 13 pp 1980ndash1993 2010

[25] H Guo Q Hu and T Jiang ldquoAnnual and seasonal streamflowresponses to climate and land-cover changes in the Poyang Lakebasin Chinardquo Journal of Hydrology vol 355 no 1ndash4 pp 106ndash122 2008

[26] L Tang D Yang H Hu and B Gao ldquoDetecting the effect ofland-use change on streamflow sediment and nutrient losses bydistributed hydrological simulationrdquo Journal of Hydrology vol409 no 1-2 pp 172ndash182 2011

[27] M D Tomer and K E Schilling ldquoA simple approach todistinguish land-use and climate-change effects on watershedhydrologyrdquo Journal of Hydrology vol 376 no 1-2 pp 24ndash332009

[28] P F Juckem R J Hunt M P Anderson and D M RobertsonldquoEffects of climate and landmanagement change on streamflowin the driftless area ofWisconsinrdquo Journal of Hydrology vol 355no 1ndash4 pp 123ndash130 2008

[29] Q Feng G D Cheng and K N Endo ldquoTowards sustainabledevelopment of the environmentally degraded River Heihebasin Chinardquo Hydrological Sciences Journal vol 46 no 5 pp647ndash658 2001

[30] G-D Cheng H-L Xiao Z-M Xu J-X Li and M-F LuldquoWater issue and its countermeasure in the inland river basinsofNorthwest Chinamdasha case study inHeiheRiver Basinrdquo Journalof Glaciology and Geocryology vol 28 no 3 pp 406ndash413 2006

[31] A Jarvis H I Reuter A Nelson and E Guevara Hole-FilledSeamless SRTM Data V3 International Centre for TropicalAgriculture (CIAT) 2006

[32] X Deng Q Jiang H Su and F Wu ldquoTrace forest conversionsin Northeast China with a 1-km area percentage data modelrdquoJournal of Applied Remote Sensing vol 4 no 1 Article ID041893 pp 1ndash13 2010

[33] X Deng H Su and J Zhan ldquoIntegration of multiple datasources to simulate the dynamics of land systemsrdquo Sensors vol8 no 2 pp 620ndash634 2008

[34] X Deng J Liu Y Lin and C Shi ldquoA framework for the landuse change dynamics model compatible with rcmsrdquo Advancesin Meteorology vol 2013 Article ID 658941 7 pages 2013

[35] X Deng C Zhao and H Yan ldquoSystematic modeling ofimpacts of land use and land cover changes on regional climate

a reviewrdquo Advances in Meteorology vol 2013 Article ID 31767811 pages 2013

[36] X Deng F Yin Y Lin Q Jin and R Qu ldquoEquilibrium analyseson structural changes of land uses in Jiangxi Provincerdquo Journalof Food Agriculture and Environment vol 10 no 1 pp 846ndash8522012

[37] X Deng Q Jiang J Zhan S He and Y Lin ldquoSimulation on thedynamics of forest area changes in Northeast Chinardquo Journal ofGeographical Sciences vol 20 no 4 pp 495ndash509 2010

[38] X Deng C Zhao Y Lin et al ldquoDownscaling the impactsof large-scale LUCC on surface temperature along with IPCCRCPs a global perspectiverdquoEnergies vol 7 no 4 pp 2720ndash27392014

[39] S Neitsch J Arnold J E A Kiniry R Srinivasan and JWilliams ldquoSoil and water assessment tool userrsquos manual version2000rdquo GSWRL Report 02-06 2002

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EarthquakesJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MineralogyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geological ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geology Advances in

4 Advances in Meteorology

Table 1 Data used and sources

Data type Data sources Scale Description

DEM Shuttle Radar Topography Mission(SRTM) 90m Elevation

Land use Data Center of Chinese Academy ofScience 1 100000 The classification system contains 25

categories

Soil WestDC (httpwestdcwestgisaccn) 1 1000000Some parameters were calculated using a(Soil-Plant-Atmosphere-Water) Field andPond Hydrology model

Weather China Meteorological Administration Daily 5 weather stationsHydrological observation Hydrological yearbook Daily 4 stations

River flow Data Center of Chinese Academy ofScience 1 250000 River network diversion

Glacier WestDC (httpwestdcwestgisaccn) 1 100000 The attributes include width length anddepth

management [33] The DLS model consists of three modulesa spatial regression module that identifies the relationshipsbetween land uses and the influencing factors a scenarioanalysis module of land use changes that determines the landdemands at the regional level a spatial disaggregationmodulethat allocates land use changes from a regional level to thedisaggregated grid cells [34 35]

The DLS model simulates the spatial-temporal land usechanges with three processes scenario analysis of land usechange spatial regression analysis and spatial allocation ofland use changes The first process was carried out with thescenario analysis module which provides the data of totalland demands at the annual scale during a given period Byincluding the scenario analyses of land use changes a set ofspatially explicit simulation results of land use change can beexported by the DLS model [36] The total land use demandscan be set by several approaches such as trend analysismethods (eg linear interpolations or more sophisticatedeconometricmodels) and economicmodels In this study thetotal land demands during the simulation period were firstdetermined using trend analysis methods and then were usedto establish a scenario of land use change during 2006ndash2030

In the spatial regression module the relationships bet-ween land uses and influencing factors were analyzed viastepwise logistic regression analysis of past land use changesand their drivers [37] For each grid cell the total probabilityfor each land use type is calculated on basis of the multino-mial logistic regression at the pixel scale as follows

119875

119894119896=

1

1 + sum

119896 =119894

119890

119909119896120573119896

119894 = 119896

119890

119909119896120573119896

1 + sum

119896 =119894

119890

119909119896120573119896

119894 = 119896

(1)

where 119875119894119896

is the probability of conversion from land type 119894to 119896 in the cells under given driving factors 119883

1 119883

2 119883

119899

represent the driving factors of climate landform locationpopulation economic growth policy and other categories120573

1 120573

2sdot sdot sdot 120573

119899are the regression analysis coefficients of driving

factors for further estimation

In this study all the data of land use and the influencingfactors were prepared at the annual scale The land use datain 2000 and 2005 were used in the logistic regression Theassumed driving factors were categorized into five groupsclimate geophysics transportation location and socioeco-nomics The data of these factors in the corresponding yearswere also prepared (Table 2)

The spatial disaggregationmodule is used to spatially andexplicitly convert the land demands into land use changes atvarious locations of the study areaThe spatial disaggregationis carried out in an iterative procedure based on the proba-bility maps conversion rules historical land use maps andland demands under the scenarios The probability maps ofeach land use type were prepared with the logistic regressionresults Besides the rules of land use conversion were set foreach land use type whose value ranges from 0 to 1 A smallervalue means one land use type is more likely to be convertedto another type and vice versa The development-restrictedareas in the study area were also specified

24 Downscaling of GCM Climate Data GCMs are arguablythe best available tools formodeling future climate YetGCMsprovide information at a resolution that is too coarse tobe directly used in hydrological modeling [38] Thereforedownscaling is required to transform the low resolutionGCMoutputs to the high resolution climate features needed forhydrological simulationThe downscaling procedure is as fol-lows First the average annual precipitation and temperatureof 30 years (1980ndash2010) were calculated which were adoptedas a baseline for selecting the GCMs We considered futureclimate change scenarios for the basin (Figure 2) by usingthe spatially distributed outputs from 10GCMs under RCP45 scenario The climate projections of Max Planck Institute(MPI) were downscaled to the 3 km times 3 km grid in the studyarea and bias was corrected and climate change scenarioswere developed by MPI for Meteorology The annual meanvalues of 10GCM spatial data from 2006 to 2030 were cal-culated according to the basin perimeter and one out of tenGCMs was also selected based on the historical trends andannual averages of temperature and precipitation The MPI

Advances in Meteorology 5

Table 2 Results of logistical regression for the six land use types with 13 driving factors

Cultivated land Forest land Grassland Water Built-up area Unused land

Slope minus0000049 0000121 minus742119864 minus 05 minus776119864 minus 06 minus11119864 minus 06 00000105(minus2752)

lowastlowastlowast

(minus6365)

lowastlowastlowast

(minus1889)

lowastlowastlowast

(minus724)

lowastlowastlowast

(minus207)

lowast

(minus264)

lowastlowast

Elevation minus0000136 minus302E minus 05 0000116 minus00000149 minus59E minus 06 00000711(minus5382)lowastlowastlowast (minus1113)lowastlowastlowast (minus208)lowastlowastlowast (minus976)lowastlowastlowast (minus785)lowastlowastlowast (minus1256)

lowastlowastlowast

Rain minus0000045 00000228 00000419 00000136 14E minus 06 minus00000347(minus1609)

lowastlowastlowast

(minus761)

lowastlowastlowast

(minus679)

lowastlowastlowast

(minus805)

lowastlowastlowast

minus17 (minus554)

lowastlowastlowast

Radiation minus0000407 0000243 minus0000923 minus864119864 minus 06 116119864 minus 05 000108(minus2427)

lowastlowastlowast

(minus1355)

lowastlowastlowast

(minus2498)

lowastlowastlowast (minus086) (minus234)

lowast

(minus2891)

lowastlowastlowast

gt0∘C accumulated temperature minus00000106 00000127 0000026 minus388E minus 06 minus72E minus 07 minus00000234(minus2403)

lowastlowastlowast

(minus2678)

lowastlowastlowast

(minus2659)

lowastlowastlowast

(minus1455)

lowastlowastlowast

(minus552)

lowastlowastlowast

(minus2367)

lowastlowastlowast

Soil organic matter 00685 0232 minus0186 00152 minus000655 minus0123(minus938)

lowastlowastlowast

(minus2964)

lowastlowastlowast

(minus1158)

lowastlowastlowast

(minus346)

lowastlowastlowast

(minus303)

lowastlowast

(minus752)

lowastlowastlowast

pH value 000769 minus00131 minus000541 000171 0000719 000834(minus715)

lowastlowastlowast

(minus1132)

lowastlowastlowast

(minus228)

lowast

(minus264)

lowastlowast

(minus226)

lowast

(minus347)

lowastlowastlowast

Population density 0000247 minus782119864 minus 05 minus316119864 minus 05 00000206 minus32119864 minus 06 minus0000154(minus3218)

lowastlowastlowast

(minus952)

lowastlowastlowast (minus187) (minus446)

lowastlowastlowast (minus140) (minus900)

lowastlowastlowast

Per capita GDP 0000548 minus308119864 minus 05 minus0000185 minus000002 0000411 minus0000723(minus7602)

lowastlowastlowast

(minus399)

lowastlowastlowast

(minus1162)

lowastlowastlowast

(minus461)

lowastlowastlowast

(minus19254)

lowastlowastlowast

(minus4487)

lowastlowastlowast

Distance to railway 0000193 0000229 0000979 000000653 172E minus 05 minus000142(minus1355)

lowastlowastlowast

(minus1496)

lowastlowastlowast

(minus3116)

lowastlowastlowast

minus076 (minus407)

lowastlowastlowast

(minus4471)

lowastlowastlowast

Distance to road minus0000221 minus0000335 000111 minus0000066 minus14119864 minus 05 minus0000477(minus1845)

lowastlowastlowast

(minus2606)

lowastlowastlowast

(minus4208)

lowastlowastlowast

(minus913)

lowastlowastlowast

(minus379)

lowastlowastlowast

(minus1779)

lowastlowastlowast

Distance to water source minus0000338 00000239 0000926 minus0000346 minus52119864 minus 06 minus0000261(minus1446)

lowastlowastlowast

minus095 (minus1796)

lowastlowastlowast

(minus2455)

lowastlowastlowast (minus075) (minus499)

lowastlowastlowast

Distance to city minus0000125 minus0000108 minus0000567 00000996 minus2119864 minus 05 000072(minus811)

lowastlowastlowast

(minus658)

lowastlowastlowast

(minus1676)

lowastlowastlowast

(minus1078)

lowastlowastlowast

(minus431)

lowastlowastlowast

(minus2098)

lowastlowastlowast

Note 119905 statistics in parentheses lowastsignificant at 10 lowastlowastsignificant at 5 lowastlowastlowastsignificant at 1

Observed

ACCESS

BCC

BNU

CanESM

CESM

FGOALS

MIROC

MPI

Fitted lineYear

Tem

pera

ture

(∘C)

35

40

45

50

55

60

65

70

75

80

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

(a)

ACCESS

BCCBNU

CanESM

CESMFGOALS

MIROC

MPILinear fit of B

Year

Prec

ipita

tion

(mm

yea

r)

500

450

400

350

300

250

200

150

100

Rain

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

(b)

Figure 2 The trends in (a) observed (grey line running average in black) and projected (10AR5 GCMs colored dash lines) temperature (b)observed and projected precipitation

6 Advances in Meteorology

Table 3 List of calibration parameters and the optimized values

Parameter Description Range Optimized valueTLAPS Temperature lapse rate [∘Ckm] 0 minus10 minus38PLAPS Precipitation lapse rate [mmH2Okm] 0 100 58SFTMP Snowfall temperature [∘C] minus2 +2 09SMTMP Snow melt base temperature [∘C] minus5 +5 21SNOEB Initial snow water content in elevation band [mm] 50 230 100TIMP Snowpack temperature lag factor 038ndash062 049SMFMN Melt factor for snow on December 21 [mmH2O

∘C-day] 305ndash351 325SMFMX Melt factor for snow on June 21 [mmH2O

∘C-day] 585ndash627 602SURLAG Surface runoff lag time [days] 418ndash519 468

model was finally chosen through comparison The result ofMPI model originates in MPI and the spatial resolution is1865∘ (LAT) times 1875∘ (LON) Then the parameters in MPIGCM were transformed into the forcing data of a regionalclimate model in weather research and forecasting (WRF)simulation and thereafter dynamical downscaling simulationwas performed at the spatial resolution of 3 km for the periodof 2006ndash2030We considered the impacts of land use changeson regional climate and the land cover data before WRFsimulation were dynamically replaced with land use changedata based on the simulation with the DLSmodel Finally thedata simulated by the regional climate model were matchedwith meteorological sites and the meteorological site datawere prepared for the simulation with SWAT

25 Simulation of Hydrological Cycle with the SWAT ModelThe study area was first divided into subwatersheds whichwere subdivided into hydrological response units (HRUs)Besides for each subwatershed the climate data used aretaken from the GCM grid point that is the closest to itscentroid To improve performance the SWAT model wascalibrated and validated by adjusting several parameters andcomparing the simulated streamflow with observed valuesThe most sensitive parameters were identified with the built-in sensitivity analysis tool in SWAT [39]The daily streamflowobservation data from Yingluoxia Hydrological Station in2004 were used for calibration and the observation data in2005 were used for validation It should be noted that thefirst three years were used as a warm-up period to mitigatethe effects of unknown initial conditions which were thenexcluded from the subsequent analysis The ability of theSWATmodel to replicate the temporal trends in the historicalhydrological observations was assessed using the coefficientof determination (1198772) the Nash and Sutcliffe (1970) modelefficiency (NSE) and the root mean square error (RMSE)

3 Result and Discussion

31 Calibration and Validation The SWAT model was cali-brated for 2004 and validated for 2005 using the daily stream-flow observation data from four gauging stations withinthe study area Finally fifteen parameters were selected forthe calibration (Table 2) which are associated with snow

(SFTMP SMTMP SMFMX SMFMN and TIMP) runoff(CN2) groundwater (ALPHA BF and GW DELAY) soil(SOL AWC) channel (CH N and CH K2) and evaporation(ESCO) processes After the sensitivity analysis 9 relativelymore sensitive parameters were identified for the calibrationMost of the parameters were adjusted based on multipletrials and the SWATmodel was calibrated using an automaticcalibration technique with the program Sequential Uncer-tainty Fitting Version (SUFI-2)With SUFI-2 sensitive initialand default parameters related to hydrology varied simulta-neously until an optimal solution was achieved The mostsensitive parameters with their best ranges and best-fittedvalues are shown in Table 3 Finally these best-fitted valueswere used to adjust the initial model inputs for the simulationduring 2006ndash2030 The model was validated using dailystreamflow observation data from the Yingluoxia Hydrologi-cal Station in 2005 The validation results show that the NSEis 078 and 1198772 of the observed and simulated data is 081(Figure 3) demonstrating the high behavioral performanceof the SWAT model

32 Future Climate under the RCP 45 Scenario Based on thedownscaled GCM climate data we calculated the mean tem-perature and precipitation of the 9 lattice points around thegrid that included Qilian meteorological station The resultswere compared with the mean monthly temperatures andprecipitation of the meteorological station during 1981ndash2005The monthly mean temperatures of the 25 years ranged fromminus3 to 3∘C and increased by around 08∘CThe mean monthlyprecipitation ranges from minus03 to 10 and increased byaround 78 (Figure 4) The increase range of mean monthlyprecipitation is large while the range of reduction is smaller

33 Future Land Use Change Simulated with DLS The resultssuggested the change in one land use type was influencedby multiple factors and the 13 driving factors can reason-ably explain the spatial patterns of all land use types Forexample the existence of forest land was significantly influ-enced by all the 13 driving factors while the existence ofcultivated land and grassland was affected by the altitudedistance and soil factors The future land uses for 2006ndash2030 were simulated with the DLS model by combining

Advances in Meteorology 7

Calibration in 2004

Sim

ulat

ion

(m3s

)

Measurement (m3s)

minus50

0 50 100 150 200 250 300 350

0

50

100

150

200

250

300

350

R2= 085

Ens = 082

(a)

Validation in 2005

Sim

ulat

ion

(m3s

)Measurement (m3

s)0 50 100 150 200 250 300

0

50

100

150

200

250

R2= 081

Ens = 078

(b)

Figure 3 The calibration and validation on streamflow of the SWAT

Trend curve

(∘C)

minus3

minus2

minus1

0

1

2

3

0 50 100 150 200 250 300

(a)

()

minus2

0

2

4

6

8

10

0 50 100 150 200 250 300

(b)

Figure 4 The difference of average monthly temperature and precipitation between 1981ndash2005 and 2006ndash2030 in Qilian Weather observedstation

the probability maps prepared with logistic regression anal-ysis the land demands under different scenarios and themap of development-restricted areas The simulation resultsindicated that the most dramatic land use changes during2006ndash2030 will mainly occur in the upper reach and some

parts of the middle reach of Heihe River Basin Comparedto 2005 the areas of forest land and unused land in 2030 willdecrease by 62 and 16 respectively while the areas ofbuilt-up land cultivated land and grassland will increase by17 13 and 48 respectively (Figure 5) The significant

8 Advances in Meteorology

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E 102

∘E

2000

39∘N

38∘N

39∘N

38∘N

40∘N

(a)

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E

2005

39∘N

38∘N

39∘N

38∘N

(b)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2010

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

38∘N

39∘N

40∘N

38∘N

(c)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2030

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

40∘N

38∘N

39∘N

38∘N

(d)

Figure 5 Comparison of the land uses interpreted in 2000 2005 and simulated in 2010 2030

increase of grassland area may mainly result from the steadypasture construction and this uptrend may continue in thefuture owing to the increasing demand for pasture products

34 Impacts of Climate and Land Use Changes on WatershedHydrology Four simulation experiments were designedbased on the land use data and climate data In the baselineexperiment for the period during 1981ndash2005 the wateryield was simulated with the land use data in 2000 2005and the weather station observations during 1981ndash2005(Figure 6(b)) Then three scenarios for the period during2006ndash2030 were designed based on the land use and climatechange (Figure 6(a)) the results from which were comparedwith that in the baseline experiment In the first scenarioduring 2006ndash2030 the water yield was simulation with theland use data in 2010 and 2030 temperature data during2006ndash2030 and the precipitation data during 1981ndash2005Thesimulation result shows that the impacts of future land use

change on the water yield vary with seasons and the landuse change will have negative overall influence on the wateryield with an influence degree of minus18 according to theannual mean water yield

The second scenario during 2006ndash2030 was based onscenarios of temperature and land use changes The secondexperiment used the land use data in 2010 and 2030 scenariodata of temperature during 2006ndash2030 and precipitation dataduring 1981ndash2005 The analysis of climate change scenariosshows that the average temperature will rise by 08∘C between1981ndash2005 and 2006ndash2030 The simulation result in thesecond experiment shows that the land use and temperaturechanges will make the water yield change by 06ndash11 thechange range of which is relatively smaller compared to thesimulation results under the scenario with only land usechange The reasons may be that the temperature rise andmelting of a small amount of snow slightly offset the adverseeffects of land use change At the same time the higher

Advances in Meteorology 9

Case ICase II

Case III

minus80

minus60

minus40

minus20

0

20

40

60

80

()

2006

12

1

2007

12

1

2008

12

1

2009

12

1

2010

12

1

2011

12

1

2012

12

1

2013

12

1

2014

12

1

2015

12

1

2016

12

1

2017

12

1

2018

12

1

2019

12

1

2020

12

1

2021

12

1

2022

12

1

2023

12

1

2024

12

1

2025

12

1

2026

12

1

2027

12

1

2028

12

1

(a)

1981

12

1

1982

12

1

1983

12

1

1984

12

1

1985

12

1

1986

12

1

1987

12

1

1988

12

1

1989

12

1

1990

12

1

1991

12

1

1992

12

1

1993

12

1

1994

12

1

1995

12

1

1996

12

1

1997

12

1

1998

12

1

1999

12

1

2000

12

1

2001

12

1

2002

12

1

2003

12

1

(mm

)

Simulated water yield during 1981ndash2005

40

35

30

25

20

15

10

5

0

(b)

Figure 6 Comparison of water yields under three scenarios

temperatures will result in more winter precipitation in theform of rain rather than snow leading to the hydrologicconsequences including increased winter discharge a shiftin the spring snowmelt peak to earlier in the season anddecreased summer discharge

The third scenario during 2006ndash2030 involves scenariosof changes in all of land use temperature and precipitationThe land use data in 2010 and 2030 and temperature andprecipitation data during 2006ndash2030 were used under thethird scenario The simulation result shows that these threefactors jointly have positive impacts on the water yieldmaking the basin water yield increase by about 98 Theincrease of the basin water yield is mainly caused by thechange in precipitation which will increase by around 108during 2006ndash2030 in comparison to that during 1981ndash2005Overall the simulation results suggest that the basin wateryield will increase in the future under different scenarios ofclimate and land use changes

4 Discussion and Conclusions

In the Heihe River Basin the upper reaches are featuredwith the generation and use of blue water while the lowerreach and surrounding areas are characterized by naturalecosystems and a low population density LULC is defined assyndromes of human activities such as agriculture forestryand building construction and most of previous studies onlyfocused on the hydrological influence of LULC change in theupper reachThe separation between hydrological impacts ofland use and climate changes has never been studied in theupper andmiddle reaches of the Heihe River Basin Howeverwe argue that studying the hydrological processes in theupper and middle reaches is essential since water supply tothe lower reach is impacted by both the climate change andhuman activities in the upper and middle reaches

In this study we analyzed the impacts of potential climateand land use changes on the water yield in the upper and

10 Advances in Meteorology

middle reaches of Heihe River Basin based on the simulationwith the SWAT model The results show that the water yieldwas more affected by climate change than by land use changeThis indicates that the predicted increase in precipitation willexert more significant impacts on the watershed hydrologythan the predicted land use changes will However the anal-ysis of the projected streamflow changes shows that there arehigher uncertainties in the dry season compared with thewet season in the simulation with the hydrological modeland GCMs climate data It is difficult to accurately projectthe hydrological changes since there are various uncertaintiesassociated with the future Green House Gas (GHG) emissionscenarios GCM structure downscaling method LULC andhydrological models In particular water resource managersare generally confronted with complex problems in sustain-able management and conservation of water resources dueto the uncertainties in the future hydrological projectionunder climate and land use changes It is therefore crucial toconsider both land use and climate changes in water resourceplanning for the Heihe River Basin so as to mitigate theirnegative hydrological impacts and more valuable informa-tion may be provided to the water resource managers if theseuncertainties in the future hydrological projection can beeffectively reduced through advancedmodeling and research

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was financially supported by themajor researchplan of the National Natural Science Foundation of China(Grant no 91325302) National Basic Research Program ofChina (973 Program) (no 2010CB950904) and the NationalNatural Science Funds of China for Distinguished YoungScholar (Grant no 71225005)

References

[1] Z ZaludM TrnkaMDubrovsky P Hlavinka D Semeradovaand E Kocmankova ldquoClimate change impacts on selectedaspects of the Czech agricultural productionrdquo Plant ProtectionScience vol 45 pp S11ndashS19 2009

[2] E Lorencova J Frelichova E Nelson and D Vackar ldquoPast andfuture impacts of land use and climate change on agriculturalecosystem services in the Czech Republicrdquo Land Use Policy vol33 pp 183ndash194 2013

[3] RMahmood A I Quintanar G Conner et al ldquoImpacts of landuseland cover change on climate and future research prioritiesrdquoBulletin of the AmericanMeteorological Society vol 91 no 1 pp37ndash46 2010

[4] Z Ma S Kang L Zhang L Tong and X Su ldquoAnalysis ofimpacts of climate variability and human activity on streamflowfor a river basin in arid region of northwest Chinardquo Journal ofHydrology vol 352 no 3-4 pp 239ndash249 2008

[5] B R Scanlon I Jolly M Sophocleous and L Zhang ldquoGlobalimpacts of conversions from natural to agricultural ecosystems

on water resources quantity versus qualityrdquo Water ResourcesResearch vol 43 no 3 Article ID W03437 2007

[6] L M Mango A M Melesse M E McClain D Gann and S GSetegn ldquoLand use and climate change impacts on the hydrologyof the upper Mara River Basin Kenya results of a modelingstudy to support better resource managementrdquo Hydrology andEarth System Sciences vol 15 no 7 pp 2245ndash2258 2011

[7] C J Vorosmarty P Green J Salisbury and R B LammersldquoGlobal water resources vulnerability from climate change andpopulation growthrdquo Science vol 289 no 5477 pp 284ndash2882000

[8] S Qi G Sun Y Wang S G McNulty and J A M MyersldquoStreamflow response to climate and landuse changes in acoastal watershed in North Carolinardquo Transactions of theASABE vol 52 no 3 pp 739ndash749 2009

[9] J Kim J Choi C Choi and S Park ldquoImpacts of changes inclimate and land useland cover under IPCC RCP scenarios onstreamflow in theHoeya River Basin Koreardquo Science of the TotalEnvironment vol 452-453 pp 181ndash195 2013

[10] Z Li W-Z Liu X-C Zhang and F-L Zheng ldquoImpacts ofland use change and climate variability on hydrology in anagricultural catchment on the Loess Plateau of Chinardquo Journalof Hydrology vol 377 no 1-2 pp 35ndash42 2009

[11] R W Skaggs D M Amatya G Chescheir C Blanton andJ Gilliam ldquoEffect of drainage and management practices onhydrology of pine plantationrdquo in Proceedings of the InternationalConference onHydrology andManagement of ForestedWetlands2006

[12] T D Prowse S Beltaos J T Gardner et al ldquoClimate changeflow regulation and land-use effects on the hydrology of thePeace-Athabasca-Slave system Findings from the NorthernRivers Ecosystem Initiativerdquo Environmental Monitoring andAssessment vol 113 no 1ndash3 pp 167ndash197 2006

[13] B Dixon and J Earls ldquoEffects of urbanization on streamflowusing SWAT with real and simulated meteorological datardquoApplied Geography vol 35 no 1-2 pp 174ndash190 2012

[14] E Lioubimtseva R Cole J M Adams and G KapustinldquoImpacts of climate and land-cover changes in arid lands ofCentral Asiardquo Journal of Arid Environments vol 62 no 2 pp285ndash308 2005

[15] Z Wang D L Ficklin Y Zhang and M Zhang ldquoImpact ofclimate change on streamflow in the arid Shiyang River Basinof northwest Chinardquo Hydrological Processes vol 26 no 18 pp2733ndash2744 2012

[16] D R DrsquoAgostino L G Trisorio N Lamaddalena and R RagabldquoAssessing the results of scenarios of climate and land usechanges on the hydrology of an Italian catchment modellingstudyrdquo Hydrological Processes vol 24 no 19 pp 2693ndash27042010

[17] V Mishra K A Cherkauer D Niyogi et al ldquoA regional scaleassessment of land useland cover and climatic changes onwater and energy cycle in the upper Midwest United StatesrdquoInternational Journal of Climatology vol 30 no 13 pp 2025ndash2044 2010

[18] J A Vano J A Foley C J Kucharik andM T Coe ldquoEvaluatingthe seasonal and interannual variations in water balance inNorthern Wisconsin using a land surface modelrdquo Journal ofGeophysical Research G Biogeosciences vol 111 no 2 2006

[19] D Mao and K A Cherkauer ldquoImpacts of land-use changeon hydrologic responses in the Great Lakes regionrdquo Journal ofHydrology vol 374 no 1-2 pp 71ndash82 2009

Advances in Meteorology 11

[20] J-Y Park M-J Park M-J H-K Joh et al ldquoAssessment ofMIROC32 hires climate and clue-s land use change impacts onwatershed hydrology using Swatrdquo Transactions of the ASABEvol 54 no 5 pp 1713ndash1724 2011

[21] S J Kim H J Kwon G A Park and M S Lee ldquoAssessmentof land-use impact on streamflow via a grid-based modellingapproach including paddy fieldsrdquoHydrological Processes vol 19no 19 pp 3801ndash3817 2005

[22] E Lu E S Takle and J Manoj ldquoThe relationships betweenclimatic and hydrological changes in the upperMississippi riverbasin a SWAT and multi-GCM studyrdquo Journal of Hydrometeo-rology vol 11 no 2 pp 437ndash451 2010

[23] L Liu Z Liu X Ren T Fischer and Y Xu ldquoHydrologicalimpacts of climate change in the Yellow River Basin for the 21stcentury using hydrological model and statistical downscalingmodelrdquo Quaternary International vol 244 no 2 pp 211ndash2202011

[24] S Fall D Niyogi A Gluhovsky R A Pielke E Kalnay andG Rochon ldquoImpacts of land use land cover on temperaturetrends over the continental United States assessment using theNorth American Regional Reanalysisrdquo International Journal ofClimatology vol 30 no 13 pp 1980ndash1993 2010

[25] H Guo Q Hu and T Jiang ldquoAnnual and seasonal streamflowresponses to climate and land-cover changes in the Poyang Lakebasin Chinardquo Journal of Hydrology vol 355 no 1ndash4 pp 106ndash122 2008

[26] L Tang D Yang H Hu and B Gao ldquoDetecting the effect ofland-use change on streamflow sediment and nutrient losses bydistributed hydrological simulationrdquo Journal of Hydrology vol409 no 1-2 pp 172ndash182 2011

[27] M D Tomer and K E Schilling ldquoA simple approach todistinguish land-use and climate-change effects on watershedhydrologyrdquo Journal of Hydrology vol 376 no 1-2 pp 24ndash332009

[28] P F Juckem R J Hunt M P Anderson and D M RobertsonldquoEffects of climate and landmanagement change on streamflowin the driftless area ofWisconsinrdquo Journal of Hydrology vol 355no 1ndash4 pp 123ndash130 2008

[29] Q Feng G D Cheng and K N Endo ldquoTowards sustainabledevelopment of the environmentally degraded River Heihebasin Chinardquo Hydrological Sciences Journal vol 46 no 5 pp647ndash658 2001

[30] G-D Cheng H-L Xiao Z-M Xu J-X Li and M-F LuldquoWater issue and its countermeasure in the inland river basinsofNorthwest Chinamdasha case study inHeiheRiver Basinrdquo Journalof Glaciology and Geocryology vol 28 no 3 pp 406ndash413 2006

[31] A Jarvis H I Reuter A Nelson and E Guevara Hole-FilledSeamless SRTM Data V3 International Centre for TropicalAgriculture (CIAT) 2006

[32] X Deng Q Jiang H Su and F Wu ldquoTrace forest conversionsin Northeast China with a 1-km area percentage data modelrdquoJournal of Applied Remote Sensing vol 4 no 1 Article ID041893 pp 1ndash13 2010

[33] X Deng H Su and J Zhan ldquoIntegration of multiple datasources to simulate the dynamics of land systemsrdquo Sensors vol8 no 2 pp 620ndash634 2008

[34] X Deng J Liu Y Lin and C Shi ldquoA framework for the landuse change dynamics model compatible with rcmsrdquo Advancesin Meteorology vol 2013 Article ID 658941 7 pages 2013

[35] X Deng C Zhao and H Yan ldquoSystematic modeling ofimpacts of land use and land cover changes on regional climate

a reviewrdquo Advances in Meteorology vol 2013 Article ID 31767811 pages 2013

[36] X Deng F Yin Y Lin Q Jin and R Qu ldquoEquilibrium analyseson structural changes of land uses in Jiangxi Provincerdquo Journalof Food Agriculture and Environment vol 10 no 1 pp 846ndash8522012

[37] X Deng Q Jiang J Zhan S He and Y Lin ldquoSimulation on thedynamics of forest area changes in Northeast Chinardquo Journal ofGeographical Sciences vol 20 no 4 pp 495ndash509 2010

[38] X Deng C Zhao Y Lin et al ldquoDownscaling the impactsof large-scale LUCC on surface temperature along with IPCCRCPs a global perspectiverdquoEnergies vol 7 no 4 pp 2720ndash27392014

[39] S Neitsch J Arnold J E A Kiniry R Srinivasan and JWilliams ldquoSoil and water assessment tool userrsquos manual version2000rdquo GSWRL Report 02-06 2002

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EarthquakesJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MineralogyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geological ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geology Advances in

Advances in Meteorology 5

Table 2 Results of logistical regression for the six land use types with 13 driving factors

Cultivated land Forest land Grassland Water Built-up area Unused land

Slope minus0000049 0000121 minus742119864 minus 05 minus776119864 minus 06 minus11119864 minus 06 00000105(minus2752)

lowastlowastlowast

(minus6365)

lowastlowastlowast

(minus1889)

lowastlowastlowast

(minus724)

lowastlowastlowast

(minus207)

lowast

(minus264)

lowastlowast

Elevation minus0000136 minus302E minus 05 0000116 minus00000149 minus59E minus 06 00000711(minus5382)lowastlowastlowast (minus1113)lowastlowastlowast (minus208)lowastlowastlowast (minus976)lowastlowastlowast (minus785)lowastlowastlowast (minus1256)

lowastlowastlowast

Rain minus0000045 00000228 00000419 00000136 14E minus 06 minus00000347(minus1609)

lowastlowastlowast

(minus761)

lowastlowastlowast

(minus679)

lowastlowastlowast

(minus805)

lowastlowastlowast

minus17 (minus554)

lowastlowastlowast

Radiation minus0000407 0000243 minus0000923 minus864119864 minus 06 116119864 minus 05 000108(minus2427)

lowastlowastlowast

(minus1355)

lowastlowastlowast

(minus2498)

lowastlowastlowast (minus086) (minus234)

lowast

(minus2891)

lowastlowastlowast

gt0∘C accumulated temperature minus00000106 00000127 0000026 minus388E minus 06 minus72E minus 07 minus00000234(minus2403)

lowastlowastlowast

(minus2678)

lowastlowastlowast

(minus2659)

lowastlowastlowast

(minus1455)

lowastlowastlowast

(minus552)

lowastlowastlowast

(minus2367)

lowastlowastlowast

Soil organic matter 00685 0232 minus0186 00152 minus000655 minus0123(minus938)

lowastlowastlowast

(minus2964)

lowastlowastlowast

(minus1158)

lowastlowastlowast

(minus346)

lowastlowastlowast

(minus303)

lowastlowast

(minus752)

lowastlowastlowast

pH value 000769 minus00131 minus000541 000171 0000719 000834(minus715)

lowastlowastlowast

(minus1132)

lowastlowastlowast

(minus228)

lowast

(minus264)

lowastlowast

(minus226)

lowast

(minus347)

lowastlowastlowast

Population density 0000247 minus782119864 minus 05 minus316119864 minus 05 00000206 minus32119864 minus 06 minus0000154(minus3218)

lowastlowastlowast

(minus952)

lowastlowastlowast (minus187) (minus446)

lowastlowastlowast (minus140) (minus900)

lowastlowastlowast

Per capita GDP 0000548 minus308119864 minus 05 minus0000185 minus000002 0000411 minus0000723(minus7602)

lowastlowastlowast

(minus399)

lowastlowastlowast

(minus1162)

lowastlowastlowast

(minus461)

lowastlowastlowast

(minus19254)

lowastlowastlowast

(minus4487)

lowastlowastlowast

Distance to railway 0000193 0000229 0000979 000000653 172E minus 05 minus000142(minus1355)

lowastlowastlowast

(minus1496)

lowastlowastlowast

(minus3116)

lowastlowastlowast

minus076 (minus407)

lowastlowastlowast

(minus4471)

lowastlowastlowast

Distance to road minus0000221 minus0000335 000111 minus0000066 minus14119864 minus 05 minus0000477(minus1845)

lowastlowastlowast

(minus2606)

lowastlowastlowast

(minus4208)

lowastlowastlowast

(minus913)

lowastlowastlowast

(minus379)

lowastlowastlowast

(minus1779)

lowastlowastlowast

Distance to water source minus0000338 00000239 0000926 minus0000346 minus52119864 minus 06 minus0000261(minus1446)

lowastlowastlowast

minus095 (minus1796)

lowastlowastlowast

(minus2455)

lowastlowastlowast (minus075) (minus499)

lowastlowastlowast

Distance to city minus0000125 minus0000108 minus0000567 00000996 minus2119864 minus 05 000072(minus811)

lowastlowastlowast

(minus658)

lowastlowastlowast

(minus1676)

lowastlowastlowast

(minus1078)

lowastlowastlowast

(minus431)

lowastlowastlowast

(minus2098)

lowastlowastlowast

Note 119905 statistics in parentheses lowastsignificant at 10 lowastlowastsignificant at 5 lowastlowastlowastsignificant at 1

Observed

ACCESS

BCC

BNU

CanESM

CESM

FGOALS

MIROC

MPI

Fitted lineYear

Tem

pera

ture

(∘C)

35

40

45

50

55

60

65

70

75

80

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

(a)

ACCESS

BCCBNU

CanESM

CESMFGOALS

MIROC

MPILinear fit of B

Year

Prec

ipita

tion

(mm

yea

r)

500

450

400

350

300

250

200

150

100

Rain

1980

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

(b)

Figure 2 The trends in (a) observed (grey line running average in black) and projected (10AR5 GCMs colored dash lines) temperature (b)observed and projected precipitation

6 Advances in Meteorology

Table 3 List of calibration parameters and the optimized values

Parameter Description Range Optimized valueTLAPS Temperature lapse rate [∘Ckm] 0 minus10 minus38PLAPS Precipitation lapse rate [mmH2Okm] 0 100 58SFTMP Snowfall temperature [∘C] minus2 +2 09SMTMP Snow melt base temperature [∘C] minus5 +5 21SNOEB Initial snow water content in elevation band [mm] 50 230 100TIMP Snowpack temperature lag factor 038ndash062 049SMFMN Melt factor for snow on December 21 [mmH2O

∘C-day] 305ndash351 325SMFMX Melt factor for snow on June 21 [mmH2O

∘C-day] 585ndash627 602SURLAG Surface runoff lag time [days] 418ndash519 468

model was finally chosen through comparison The result ofMPI model originates in MPI and the spatial resolution is1865∘ (LAT) times 1875∘ (LON) Then the parameters in MPIGCM were transformed into the forcing data of a regionalclimate model in weather research and forecasting (WRF)simulation and thereafter dynamical downscaling simulationwas performed at the spatial resolution of 3 km for the periodof 2006ndash2030We considered the impacts of land use changeson regional climate and the land cover data before WRFsimulation were dynamically replaced with land use changedata based on the simulation with the DLSmodel Finally thedata simulated by the regional climate model were matchedwith meteorological sites and the meteorological site datawere prepared for the simulation with SWAT

25 Simulation of Hydrological Cycle with the SWAT ModelThe study area was first divided into subwatersheds whichwere subdivided into hydrological response units (HRUs)Besides for each subwatershed the climate data used aretaken from the GCM grid point that is the closest to itscentroid To improve performance the SWAT model wascalibrated and validated by adjusting several parameters andcomparing the simulated streamflow with observed valuesThe most sensitive parameters were identified with the built-in sensitivity analysis tool in SWAT [39]The daily streamflowobservation data from Yingluoxia Hydrological Station in2004 were used for calibration and the observation data in2005 were used for validation It should be noted that thefirst three years were used as a warm-up period to mitigatethe effects of unknown initial conditions which were thenexcluded from the subsequent analysis The ability of theSWATmodel to replicate the temporal trends in the historicalhydrological observations was assessed using the coefficientof determination (1198772) the Nash and Sutcliffe (1970) modelefficiency (NSE) and the root mean square error (RMSE)

3 Result and Discussion

31 Calibration and Validation The SWAT model was cali-brated for 2004 and validated for 2005 using the daily stream-flow observation data from four gauging stations withinthe study area Finally fifteen parameters were selected forthe calibration (Table 2) which are associated with snow

(SFTMP SMTMP SMFMX SMFMN and TIMP) runoff(CN2) groundwater (ALPHA BF and GW DELAY) soil(SOL AWC) channel (CH N and CH K2) and evaporation(ESCO) processes After the sensitivity analysis 9 relativelymore sensitive parameters were identified for the calibrationMost of the parameters were adjusted based on multipletrials and the SWATmodel was calibrated using an automaticcalibration technique with the program Sequential Uncer-tainty Fitting Version (SUFI-2)With SUFI-2 sensitive initialand default parameters related to hydrology varied simulta-neously until an optimal solution was achieved The mostsensitive parameters with their best ranges and best-fittedvalues are shown in Table 3 Finally these best-fitted valueswere used to adjust the initial model inputs for the simulationduring 2006ndash2030 The model was validated using dailystreamflow observation data from the Yingluoxia Hydrologi-cal Station in 2005 The validation results show that the NSEis 078 and 1198772 of the observed and simulated data is 081(Figure 3) demonstrating the high behavioral performanceof the SWAT model

32 Future Climate under the RCP 45 Scenario Based on thedownscaled GCM climate data we calculated the mean tem-perature and precipitation of the 9 lattice points around thegrid that included Qilian meteorological station The resultswere compared with the mean monthly temperatures andprecipitation of the meteorological station during 1981ndash2005The monthly mean temperatures of the 25 years ranged fromminus3 to 3∘C and increased by around 08∘CThe mean monthlyprecipitation ranges from minus03 to 10 and increased byaround 78 (Figure 4) The increase range of mean monthlyprecipitation is large while the range of reduction is smaller

33 Future Land Use Change Simulated with DLS The resultssuggested the change in one land use type was influencedby multiple factors and the 13 driving factors can reason-ably explain the spatial patterns of all land use types Forexample the existence of forest land was significantly influ-enced by all the 13 driving factors while the existence ofcultivated land and grassland was affected by the altitudedistance and soil factors The future land uses for 2006ndash2030 were simulated with the DLS model by combining

Advances in Meteorology 7

Calibration in 2004

Sim

ulat

ion

(m3s

)

Measurement (m3s)

minus50

0 50 100 150 200 250 300 350

0

50

100

150

200

250

300

350

R2= 085

Ens = 082

(a)

Validation in 2005

Sim

ulat

ion

(m3s

)Measurement (m3

s)0 50 100 150 200 250 300

0

50

100

150

200

250

R2= 081

Ens = 078

(b)

Figure 3 The calibration and validation on streamflow of the SWAT

Trend curve

(∘C)

minus3

minus2

minus1

0

1

2

3

0 50 100 150 200 250 300

(a)

()

minus2

0

2

4

6

8

10

0 50 100 150 200 250 300

(b)

Figure 4 The difference of average monthly temperature and precipitation between 1981ndash2005 and 2006ndash2030 in Qilian Weather observedstation

the probability maps prepared with logistic regression anal-ysis the land demands under different scenarios and themap of development-restricted areas The simulation resultsindicated that the most dramatic land use changes during2006ndash2030 will mainly occur in the upper reach and some

parts of the middle reach of Heihe River Basin Comparedto 2005 the areas of forest land and unused land in 2030 willdecrease by 62 and 16 respectively while the areas ofbuilt-up land cultivated land and grassland will increase by17 13 and 48 respectively (Figure 5) The significant

8 Advances in Meteorology

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E 102

∘E

2000

39∘N

38∘N

39∘N

38∘N

40∘N

(a)

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E

2005

39∘N

38∘N

39∘N

38∘N

(b)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2010

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

38∘N

39∘N

40∘N

38∘N

(c)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2030

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

40∘N

38∘N

39∘N

38∘N

(d)

Figure 5 Comparison of the land uses interpreted in 2000 2005 and simulated in 2010 2030

increase of grassland area may mainly result from the steadypasture construction and this uptrend may continue in thefuture owing to the increasing demand for pasture products

34 Impacts of Climate and Land Use Changes on WatershedHydrology Four simulation experiments were designedbased on the land use data and climate data In the baselineexperiment for the period during 1981ndash2005 the wateryield was simulated with the land use data in 2000 2005and the weather station observations during 1981ndash2005(Figure 6(b)) Then three scenarios for the period during2006ndash2030 were designed based on the land use and climatechange (Figure 6(a)) the results from which were comparedwith that in the baseline experiment In the first scenarioduring 2006ndash2030 the water yield was simulation with theland use data in 2010 and 2030 temperature data during2006ndash2030 and the precipitation data during 1981ndash2005Thesimulation result shows that the impacts of future land use

change on the water yield vary with seasons and the landuse change will have negative overall influence on the wateryield with an influence degree of minus18 according to theannual mean water yield

The second scenario during 2006ndash2030 was based onscenarios of temperature and land use changes The secondexperiment used the land use data in 2010 and 2030 scenariodata of temperature during 2006ndash2030 and precipitation dataduring 1981ndash2005 The analysis of climate change scenariosshows that the average temperature will rise by 08∘C between1981ndash2005 and 2006ndash2030 The simulation result in thesecond experiment shows that the land use and temperaturechanges will make the water yield change by 06ndash11 thechange range of which is relatively smaller compared to thesimulation results under the scenario with only land usechange The reasons may be that the temperature rise andmelting of a small amount of snow slightly offset the adverseeffects of land use change At the same time the higher

Advances in Meteorology 9

Case ICase II

Case III

minus80

minus60

minus40

minus20

0

20

40

60

80

()

2006

12

1

2007

12

1

2008

12

1

2009

12

1

2010

12

1

2011

12

1

2012

12

1

2013

12

1

2014

12

1

2015

12

1

2016

12

1

2017

12

1

2018

12

1

2019

12

1

2020

12

1

2021

12

1

2022

12

1

2023

12

1

2024

12

1

2025

12

1

2026

12

1

2027

12

1

2028

12

1

(a)

1981

12

1

1982

12

1

1983

12

1

1984

12

1

1985

12

1

1986

12

1

1987

12

1

1988

12

1

1989

12

1

1990

12

1

1991

12

1

1992

12

1

1993

12

1

1994

12

1

1995

12

1

1996

12

1

1997

12

1

1998

12

1

1999

12

1

2000

12

1

2001

12

1

2002

12

1

2003

12

1

(mm

)

Simulated water yield during 1981ndash2005

40

35

30

25

20

15

10

5

0

(b)

Figure 6 Comparison of water yields under three scenarios

temperatures will result in more winter precipitation in theform of rain rather than snow leading to the hydrologicconsequences including increased winter discharge a shiftin the spring snowmelt peak to earlier in the season anddecreased summer discharge

The third scenario during 2006ndash2030 involves scenariosof changes in all of land use temperature and precipitationThe land use data in 2010 and 2030 and temperature andprecipitation data during 2006ndash2030 were used under thethird scenario The simulation result shows that these threefactors jointly have positive impacts on the water yieldmaking the basin water yield increase by about 98 Theincrease of the basin water yield is mainly caused by thechange in precipitation which will increase by around 108during 2006ndash2030 in comparison to that during 1981ndash2005Overall the simulation results suggest that the basin wateryield will increase in the future under different scenarios ofclimate and land use changes

4 Discussion and Conclusions

In the Heihe River Basin the upper reaches are featuredwith the generation and use of blue water while the lowerreach and surrounding areas are characterized by naturalecosystems and a low population density LULC is defined assyndromes of human activities such as agriculture forestryand building construction and most of previous studies onlyfocused on the hydrological influence of LULC change in theupper reachThe separation between hydrological impacts ofland use and climate changes has never been studied in theupper andmiddle reaches of the Heihe River Basin Howeverwe argue that studying the hydrological processes in theupper and middle reaches is essential since water supply tothe lower reach is impacted by both the climate change andhuman activities in the upper and middle reaches

In this study we analyzed the impacts of potential climateand land use changes on the water yield in the upper and

10 Advances in Meteorology

middle reaches of Heihe River Basin based on the simulationwith the SWAT model The results show that the water yieldwas more affected by climate change than by land use changeThis indicates that the predicted increase in precipitation willexert more significant impacts on the watershed hydrologythan the predicted land use changes will However the anal-ysis of the projected streamflow changes shows that there arehigher uncertainties in the dry season compared with thewet season in the simulation with the hydrological modeland GCMs climate data It is difficult to accurately projectthe hydrological changes since there are various uncertaintiesassociated with the future Green House Gas (GHG) emissionscenarios GCM structure downscaling method LULC andhydrological models In particular water resource managersare generally confronted with complex problems in sustain-able management and conservation of water resources dueto the uncertainties in the future hydrological projectionunder climate and land use changes It is therefore crucial toconsider both land use and climate changes in water resourceplanning for the Heihe River Basin so as to mitigate theirnegative hydrological impacts and more valuable informa-tion may be provided to the water resource managers if theseuncertainties in the future hydrological projection can beeffectively reduced through advancedmodeling and research

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was financially supported by themajor researchplan of the National Natural Science Foundation of China(Grant no 91325302) National Basic Research Program ofChina (973 Program) (no 2010CB950904) and the NationalNatural Science Funds of China for Distinguished YoungScholar (Grant no 71225005)

References

[1] Z ZaludM TrnkaMDubrovsky P Hlavinka D Semeradovaand E Kocmankova ldquoClimate change impacts on selectedaspects of the Czech agricultural productionrdquo Plant ProtectionScience vol 45 pp S11ndashS19 2009

[2] E Lorencova J Frelichova E Nelson and D Vackar ldquoPast andfuture impacts of land use and climate change on agriculturalecosystem services in the Czech Republicrdquo Land Use Policy vol33 pp 183ndash194 2013

[3] RMahmood A I Quintanar G Conner et al ldquoImpacts of landuseland cover change on climate and future research prioritiesrdquoBulletin of the AmericanMeteorological Society vol 91 no 1 pp37ndash46 2010

[4] Z Ma S Kang L Zhang L Tong and X Su ldquoAnalysis ofimpacts of climate variability and human activity on streamflowfor a river basin in arid region of northwest Chinardquo Journal ofHydrology vol 352 no 3-4 pp 239ndash249 2008

[5] B R Scanlon I Jolly M Sophocleous and L Zhang ldquoGlobalimpacts of conversions from natural to agricultural ecosystems

on water resources quantity versus qualityrdquo Water ResourcesResearch vol 43 no 3 Article ID W03437 2007

[6] L M Mango A M Melesse M E McClain D Gann and S GSetegn ldquoLand use and climate change impacts on the hydrologyof the upper Mara River Basin Kenya results of a modelingstudy to support better resource managementrdquo Hydrology andEarth System Sciences vol 15 no 7 pp 2245ndash2258 2011

[7] C J Vorosmarty P Green J Salisbury and R B LammersldquoGlobal water resources vulnerability from climate change andpopulation growthrdquo Science vol 289 no 5477 pp 284ndash2882000

[8] S Qi G Sun Y Wang S G McNulty and J A M MyersldquoStreamflow response to climate and landuse changes in acoastal watershed in North Carolinardquo Transactions of theASABE vol 52 no 3 pp 739ndash749 2009

[9] J Kim J Choi C Choi and S Park ldquoImpacts of changes inclimate and land useland cover under IPCC RCP scenarios onstreamflow in theHoeya River Basin Koreardquo Science of the TotalEnvironment vol 452-453 pp 181ndash195 2013

[10] Z Li W-Z Liu X-C Zhang and F-L Zheng ldquoImpacts ofland use change and climate variability on hydrology in anagricultural catchment on the Loess Plateau of Chinardquo Journalof Hydrology vol 377 no 1-2 pp 35ndash42 2009

[11] R W Skaggs D M Amatya G Chescheir C Blanton andJ Gilliam ldquoEffect of drainage and management practices onhydrology of pine plantationrdquo in Proceedings of the InternationalConference onHydrology andManagement of ForestedWetlands2006

[12] T D Prowse S Beltaos J T Gardner et al ldquoClimate changeflow regulation and land-use effects on the hydrology of thePeace-Athabasca-Slave system Findings from the NorthernRivers Ecosystem Initiativerdquo Environmental Monitoring andAssessment vol 113 no 1ndash3 pp 167ndash197 2006

[13] B Dixon and J Earls ldquoEffects of urbanization on streamflowusing SWAT with real and simulated meteorological datardquoApplied Geography vol 35 no 1-2 pp 174ndash190 2012

[14] E Lioubimtseva R Cole J M Adams and G KapustinldquoImpacts of climate and land-cover changes in arid lands ofCentral Asiardquo Journal of Arid Environments vol 62 no 2 pp285ndash308 2005

[15] Z Wang D L Ficklin Y Zhang and M Zhang ldquoImpact ofclimate change on streamflow in the arid Shiyang River Basinof northwest Chinardquo Hydrological Processes vol 26 no 18 pp2733ndash2744 2012

[16] D R DrsquoAgostino L G Trisorio N Lamaddalena and R RagabldquoAssessing the results of scenarios of climate and land usechanges on the hydrology of an Italian catchment modellingstudyrdquo Hydrological Processes vol 24 no 19 pp 2693ndash27042010

[17] V Mishra K A Cherkauer D Niyogi et al ldquoA regional scaleassessment of land useland cover and climatic changes onwater and energy cycle in the upper Midwest United StatesrdquoInternational Journal of Climatology vol 30 no 13 pp 2025ndash2044 2010

[18] J A Vano J A Foley C J Kucharik andM T Coe ldquoEvaluatingthe seasonal and interannual variations in water balance inNorthern Wisconsin using a land surface modelrdquo Journal ofGeophysical Research G Biogeosciences vol 111 no 2 2006

[19] D Mao and K A Cherkauer ldquoImpacts of land-use changeon hydrologic responses in the Great Lakes regionrdquo Journal ofHydrology vol 374 no 1-2 pp 71ndash82 2009

Advances in Meteorology 11

[20] J-Y Park M-J Park M-J H-K Joh et al ldquoAssessment ofMIROC32 hires climate and clue-s land use change impacts onwatershed hydrology using Swatrdquo Transactions of the ASABEvol 54 no 5 pp 1713ndash1724 2011

[21] S J Kim H J Kwon G A Park and M S Lee ldquoAssessmentof land-use impact on streamflow via a grid-based modellingapproach including paddy fieldsrdquoHydrological Processes vol 19no 19 pp 3801ndash3817 2005

[22] E Lu E S Takle and J Manoj ldquoThe relationships betweenclimatic and hydrological changes in the upperMississippi riverbasin a SWAT and multi-GCM studyrdquo Journal of Hydrometeo-rology vol 11 no 2 pp 437ndash451 2010

[23] L Liu Z Liu X Ren T Fischer and Y Xu ldquoHydrologicalimpacts of climate change in the Yellow River Basin for the 21stcentury using hydrological model and statistical downscalingmodelrdquo Quaternary International vol 244 no 2 pp 211ndash2202011

[24] S Fall D Niyogi A Gluhovsky R A Pielke E Kalnay andG Rochon ldquoImpacts of land use land cover on temperaturetrends over the continental United States assessment using theNorth American Regional Reanalysisrdquo International Journal ofClimatology vol 30 no 13 pp 1980ndash1993 2010

[25] H Guo Q Hu and T Jiang ldquoAnnual and seasonal streamflowresponses to climate and land-cover changes in the Poyang Lakebasin Chinardquo Journal of Hydrology vol 355 no 1ndash4 pp 106ndash122 2008

[26] L Tang D Yang H Hu and B Gao ldquoDetecting the effect ofland-use change on streamflow sediment and nutrient losses bydistributed hydrological simulationrdquo Journal of Hydrology vol409 no 1-2 pp 172ndash182 2011

[27] M D Tomer and K E Schilling ldquoA simple approach todistinguish land-use and climate-change effects on watershedhydrologyrdquo Journal of Hydrology vol 376 no 1-2 pp 24ndash332009

[28] P F Juckem R J Hunt M P Anderson and D M RobertsonldquoEffects of climate and landmanagement change on streamflowin the driftless area ofWisconsinrdquo Journal of Hydrology vol 355no 1ndash4 pp 123ndash130 2008

[29] Q Feng G D Cheng and K N Endo ldquoTowards sustainabledevelopment of the environmentally degraded River Heihebasin Chinardquo Hydrological Sciences Journal vol 46 no 5 pp647ndash658 2001

[30] G-D Cheng H-L Xiao Z-M Xu J-X Li and M-F LuldquoWater issue and its countermeasure in the inland river basinsofNorthwest Chinamdasha case study inHeiheRiver Basinrdquo Journalof Glaciology and Geocryology vol 28 no 3 pp 406ndash413 2006

[31] A Jarvis H I Reuter A Nelson and E Guevara Hole-FilledSeamless SRTM Data V3 International Centre for TropicalAgriculture (CIAT) 2006

[32] X Deng Q Jiang H Su and F Wu ldquoTrace forest conversionsin Northeast China with a 1-km area percentage data modelrdquoJournal of Applied Remote Sensing vol 4 no 1 Article ID041893 pp 1ndash13 2010

[33] X Deng H Su and J Zhan ldquoIntegration of multiple datasources to simulate the dynamics of land systemsrdquo Sensors vol8 no 2 pp 620ndash634 2008

[34] X Deng J Liu Y Lin and C Shi ldquoA framework for the landuse change dynamics model compatible with rcmsrdquo Advancesin Meteorology vol 2013 Article ID 658941 7 pages 2013

[35] X Deng C Zhao and H Yan ldquoSystematic modeling ofimpacts of land use and land cover changes on regional climate

a reviewrdquo Advances in Meteorology vol 2013 Article ID 31767811 pages 2013

[36] X Deng F Yin Y Lin Q Jin and R Qu ldquoEquilibrium analyseson structural changes of land uses in Jiangxi Provincerdquo Journalof Food Agriculture and Environment vol 10 no 1 pp 846ndash8522012

[37] X Deng Q Jiang J Zhan S He and Y Lin ldquoSimulation on thedynamics of forest area changes in Northeast Chinardquo Journal ofGeographical Sciences vol 20 no 4 pp 495ndash509 2010

[38] X Deng C Zhao Y Lin et al ldquoDownscaling the impactsof large-scale LUCC on surface temperature along with IPCCRCPs a global perspectiverdquoEnergies vol 7 no 4 pp 2720ndash27392014

[39] S Neitsch J Arnold J E A Kiniry R Srinivasan and JWilliams ldquoSoil and water assessment tool userrsquos manual version2000rdquo GSWRL Report 02-06 2002

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EarthquakesJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MineralogyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geological ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geology Advances in

6 Advances in Meteorology

Table 3 List of calibration parameters and the optimized values

Parameter Description Range Optimized valueTLAPS Temperature lapse rate [∘Ckm] 0 minus10 minus38PLAPS Precipitation lapse rate [mmH2Okm] 0 100 58SFTMP Snowfall temperature [∘C] minus2 +2 09SMTMP Snow melt base temperature [∘C] minus5 +5 21SNOEB Initial snow water content in elevation band [mm] 50 230 100TIMP Snowpack temperature lag factor 038ndash062 049SMFMN Melt factor for snow on December 21 [mmH2O

∘C-day] 305ndash351 325SMFMX Melt factor for snow on June 21 [mmH2O

∘C-day] 585ndash627 602SURLAG Surface runoff lag time [days] 418ndash519 468

model was finally chosen through comparison The result ofMPI model originates in MPI and the spatial resolution is1865∘ (LAT) times 1875∘ (LON) Then the parameters in MPIGCM were transformed into the forcing data of a regionalclimate model in weather research and forecasting (WRF)simulation and thereafter dynamical downscaling simulationwas performed at the spatial resolution of 3 km for the periodof 2006ndash2030We considered the impacts of land use changeson regional climate and the land cover data before WRFsimulation were dynamically replaced with land use changedata based on the simulation with the DLSmodel Finally thedata simulated by the regional climate model were matchedwith meteorological sites and the meteorological site datawere prepared for the simulation with SWAT

25 Simulation of Hydrological Cycle with the SWAT ModelThe study area was first divided into subwatersheds whichwere subdivided into hydrological response units (HRUs)Besides for each subwatershed the climate data used aretaken from the GCM grid point that is the closest to itscentroid To improve performance the SWAT model wascalibrated and validated by adjusting several parameters andcomparing the simulated streamflow with observed valuesThe most sensitive parameters were identified with the built-in sensitivity analysis tool in SWAT [39]The daily streamflowobservation data from Yingluoxia Hydrological Station in2004 were used for calibration and the observation data in2005 were used for validation It should be noted that thefirst three years were used as a warm-up period to mitigatethe effects of unknown initial conditions which were thenexcluded from the subsequent analysis The ability of theSWATmodel to replicate the temporal trends in the historicalhydrological observations was assessed using the coefficientof determination (1198772) the Nash and Sutcliffe (1970) modelefficiency (NSE) and the root mean square error (RMSE)

3 Result and Discussion

31 Calibration and Validation The SWAT model was cali-brated for 2004 and validated for 2005 using the daily stream-flow observation data from four gauging stations withinthe study area Finally fifteen parameters were selected forthe calibration (Table 2) which are associated with snow

(SFTMP SMTMP SMFMX SMFMN and TIMP) runoff(CN2) groundwater (ALPHA BF and GW DELAY) soil(SOL AWC) channel (CH N and CH K2) and evaporation(ESCO) processes After the sensitivity analysis 9 relativelymore sensitive parameters were identified for the calibrationMost of the parameters were adjusted based on multipletrials and the SWATmodel was calibrated using an automaticcalibration technique with the program Sequential Uncer-tainty Fitting Version (SUFI-2)With SUFI-2 sensitive initialand default parameters related to hydrology varied simulta-neously until an optimal solution was achieved The mostsensitive parameters with their best ranges and best-fittedvalues are shown in Table 3 Finally these best-fitted valueswere used to adjust the initial model inputs for the simulationduring 2006ndash2030 The model was validated using dailystreamflow observation data from the Yingluoxia Hydrologi-cal Station in 2005 The validation results show that the NSEis 078 and 1198772 of the observed and simulated data is 081(Figure 3) demonstrating the high behavioral performanceof the SWAT model

32 Future Climate under the RCP 45 Scenario Based on thedownscaled GCM climate data we calculated the mean tem-perature and precipitation of the 9 lattice points around thegrid that included Qilian meteorological station The resultswere compared with the mean monthly temperatures andprecipitation of the meteorological station during 1981ndash2005The monthly mean temperatures of the 25 years ranged fromminus3 to 3∘C and increased by around 08∘CThe mean monthlyprecipitation ranges from minus03 to 10 and increased byaround 78 (Figure 4) The increase range of mean monthlyprecipitation is large while the range of reduction is smaller

33 Future Land Use Change Simulated with DLS The resultssuggested the change in one land use type was influencedby multiple factors and the 13 driving factors can reason-ably explain the spatial patterns of all land use types Forexample the existence of forest land was significantly influ-enced by all the 13 driving factors while the existence ofcultivated land and grassland was affected by the altitudedistance and soil factors The future land uses for 2006ndash2030 were simulated with the DLS model by combining

Advances in Meteorology 7

Calibration in 2004

Sim

ulat

ion

(m3s

)

Measurement (m3s)

minus50

0 50 100 150 200 250 300 350

0

50

100

150

200

250

300

350

R2= 085

Ens = 082

(a)

Validation in 2005

Sim

ulat

ion

(m3s

)Measurement (m3

s)0 50 100 150 200 250 300

0

50

100

150

200

250

R2= 081

Ens = 078

(b)

Figure 3 The calibration and validation on streamflow of the SWAT

Trend curve

(∘C)

minus3

minus2

minus1

0

1

2

3

0 50 100 150 200 250 300

(a)

()

minus2

0

2

4

6

8

10

0 50 100 150 200 250 300

(b)

Figure 4 The difference of average monthly temperature and precipitation between 1981ndash2005 and 2006ndash2030 in Qilian Weather observedstation

the probability maps prepared with logistic regression anal-ysis the land demands under different scenarios and themap of development-restricted areas The simulation resultsindicated that the most dramatic land use changes during2006ndash2030 will mainly occur in the upper reach and some

parts of the middle reach of Heihe River Basin Comparedto 2005 the areas of forest land and unused land in 2030 willdecrease by 62 and 16 respectively while the areas ofbuilt-up land cultivated land and grassland will increase by17 13 and 48 respectively (Figure 5) The significant

8 Advances in Meteorology

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E 102

∘E

2000

39∘N

38∘N

39∘N

38∘N

40∘N

(a)

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E

2005

39∘N

38∘N

39∘N

38∘N

(b)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2010

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

38∘N

39∘N

40∘N

38∘N

(c)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2030

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

40∘N

38∘N

39∘N

38∘N

(d)

Figure 5 Comparison of the land uses interpreted in 2000 2005 and simulated in 2010 2030

increase of grassland area may mainly result from the steadypasture construction and this uptrend may continue in thefuture owing to the increasing demand for pasture products

34 Impacts of Climate and Land Use Changes on WatershedHydrology Four simulation experiments were designedbased on the land use data and climate data In the baselineexperiment for the period during 1981ndash2005 the wateryield was simulated with the land use data in 2000 2005and the weather station observations during 1981ndash2005(Figure 6(b)) Then three scenarios for the period during2006ndash2030 were designed based on the land use and climatechange (Figure 6(a)) the results from which were comparedwith that in the baseline experiment In the first scenarioduring 2006ndash2030 the water yield was simulation with theland use data in 2010 and 2030 temperature data during2006ndash2030 and the precipitation data during 1981ndash2005Thesimulation result shows that the impacts of future land use

change on the water yield vary with seasons and the landuse change will have negative overall influence on the wateryield with an influence degree of minus18 according to theannual mean water yield

The second scenario during 2006ndash2030 was based onscenarios of temperature and land use changes The secondexperiment used the land use data in 2010 and 2030 scenariodata of temperature during 2006ndash2030 and precipitation dataduring 1981ndash2005 The analysis of climate change scenariosshows that the average temperature will rise by 08∘C between1981ndash2005 and 2006ndash2030 The simulation result in thesecond experiment shows that the land use and temperaturechanges will make the water yield change by 06ndash11 thechange range of which is relatively smaller compared to thesimulation results under the scenario with only land usechange The reasons may be that the temperature rise andmelting of a small amount of snow slightly offset the adverseeffects of land use change At the same time the higher

Advances in Meteorology 9

Case ICase II

Case III

minus80

minus60

minus40

minus20

0

20

40

60

80

()

2006

12

1

2007

12

1

2008

12

1

2009

12

1

2010

12

1

2011

12

1

2012

12

1

2013

12

1

2014

12

1

2015

12

1

2016

12

1

2017

12

1

2018

12

1

2019

12

1

2020

12

1

2021

12

1

2022

12

1

2023

12

1

2024

12

1

2025

12

1

2026

12

1

2027

12

1

2028

12

1

(a)

1981

12

1

1982

12

1

1983

12

1

1984

12

1

1985

12

1

1986

12

1

1987

12

1

1988

12

1

1989

12

1

1990

12

1

1991

12

1

1992

12

1

1993

12

1

1994

12

1

1995

12

1

1996

12

1

1997

12

1

1998

12

1

1999

12

1

2000

12

1

2001

12

1

2002

12

1

2003

12

1

(mm

)

Simulated water yield during 1981ndash2005

40

35

30

25

20

15

10

5

0

(b)

Figure 6 Comparison of water yields under three scenarios

temperatures will result in more winter precipitation in theform of rain rather than snow leading to the hydrologicconsequences including increased winter discharge a shiftin the spring snowmelt peak to earlier in the season anddecreased summer discharge

The third scenario during 2006ndash2030 involves scenariosof changes in all of land use temperature and precipitationThe land use data in 2010 and 2030 and temperature andprecipitation data during 2006ndash2030 were used under thethird scenario The simulation result shows that these threefactors jointly have positive impacts on the water yieldmaking the basin water yield increase by about 98 Theincrease of the basin water yield is mainly caused by thechange in precipitation which will increase by around 108during 2006ndash2030 in comparison to that during 1981ndash2005Overall the simulation results suggest that the basin wateryield will increase in the future under different scenarios ofclimate and land use changes

4 Discussion and Conclusions

In the Heihe River Basin the upper reaches are featuredwith the generation and use of blue water while the lowerreach and surrounding areas are characterized by naturalecosystems and a low population density LULC is defined assyndromes of human activities such as agriculture forestryand building construction and most of previous studies onlyfocused on the hydrological influence of LULC change in theupper reachThe separation between hydrological impacts ofland use and climate changes has never been studied in theupper andmiddle reaches of the Heihe River Basin Howeverwe argue that studying the hydrological processes in theupper and middle reaches is essential since water supply tothe lower reach is impacted by both the climate change andhuman activities in the upper and middle reaches

In this study we analyzed the impacts of potential climateand land use changes on the water yield in the upper and

10 Advances in Meteorology

middle reaches of Heihe River Basin based on the simulationwith the SWAT model The results show that the water yieldwas more affected by climate change than by land use changeThis indicates that the predicted increase in precipitation willexert more significant impacts on the watershed hydrologythan the predicted land use changes will However the anal-ysis of the projected streamflow changes shows that there arehigher uncertainties in the dry season compared with thewet season in the simulation with the hydrological modeland GCMs climate data It is difficult to accurately projectthe hydrological changes since there are various uncertaintiesassociated with the future Green House Gas (GHG) emissionscenarios GCM structure downscaling method LULC andhydrological models In particular water resource managersare generally confronted with complex problems in sustain-able management and conservation of water resources dueto the uncertainties in the future hydrological projectionunder climate and land use changes It is therefore crucial toconsider both land use and climate changes in water resourceplanning for the Heihe River Basin so as to mitigate theirnegative hydrological impacts and more valuable informa-tion may be provided to the water resource managers if theseuncertainties in the future hydrological projection can beeffectively reduced through advancedmodeling and research

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was financially supported by themajor researchplan of the National Natural Science Foundation of China(Grant no 91325302) National Basic Research Program ofChina (973 Program) (no 2010CB950904) and the NationalNatural Science Funds of China for Distinguished YoungScholar (Grant no 71225005)

References

[1] Z ZaludM TrnkaMDubrovsky P Hlavinka D Semeradovaand E Kocmankova ldquoClimate change impacts on selectedaspects of the Czech agricultural productionrdquo Plant ProtectionScience vol 45 pp S11ndashS19 2009

[2] E Lorencova J Frelichova E Nelson and D Vackar ldquoPast andfuture impacts of land use and climate change on agriculturalecosystem services in the Czech Republicrdquo Land Use Policy vol33 pp 183ndash194 2013

[3] RMahmood A I Quintanar G Conner et al ldquoImpacts of landuseland cover change on climate and future research prioritiesrdquoBulletin of the AmericanMeteorological Society vol 91 no 1 pp37ndash46 2010

[4] Z Ma S Kang L Zhang L Tong and X Su ldquoAnalysis ofimpacts of climate variability and human activity on streamflowfor a river basin in arid region of northwest Chinardquo Journal ofHydrology vol 352 no 3-4 pp 239ndash249 2008

[5] B R Scanlon I Jolly M Sophocleous and L Zhang ldquoGlobalimpacts of conversions from natural to agricultural ecosystems

on water resources quantity versus qualityrdquo Water ResourcesResearch vol 43 no 3 Article ID W03437 2007

[6] L M Mango A M Melesse M E McClain D Gann and S GSetegn ldquoLand use and climate change impacts on the hydrologyof the upper Mara River Basin Kenya results of a modelingstudy to support better resource managementrdquo Hydrology andEarth System Sciences vol 15 no 7 pp 2245ndash2258 2011

[7] C J Vorosmarty P Green J Salisbury and R B LammersldquoGlobal water resources vulnerability from climate change andpopulation growthrdquo Science vol 289 no 5477 pp 284ndash2882000

[8] S Qi G Sun Y Wang S G McNulty and J A M MyersldquoStreamflow response to climate and landuse changes in acoastal watershed in North Carolinardquo Transactions of theASABE vol 52 no 3 pp 739ndash749 2009

[9] J Kim J Choi C Choi and S Park ldquoImpacts of changes inclimate and land useland cover under IPCC RCP scenarios onstreamflow in theHoeya River Basin Koreardquo Science of the TotalEnvironment vol 452-453 pp 181ndash195 2013

[10] Z Li W-Z Liu X-C Zhang and F-L Zheng ldquoImpacts ofland use change and climate variability on hydrology in anagricultural catchment on the Loess Plateau of Chinardquo Journalof Hydrology vol 377 no 1-2 pp 35ndash42 2009

[11] R W Skaggs D M Amatya G Chescheir C Blanton andJ Gilliam ldquoEffect of drainage and management practices onhydrology of pine plantationrdquo in Proceedings of the InternationalConference onHydrology andManagement of ForestedWetlands2006

[12] T D Prowse S Beltaos J T Gardner et al ldquoClimate changeflow regulation and land-use effects on the hydrology of thePeace-Athabasca-Slave system Findings from the NorthernRivers Ecosystem Initiativerdquo Environmental Monitoring andAssessment vol 113 no 1ndash3 pp 167ndash197 2006

[13] B Dixon and J Earls ldquoEffects of urbanization on streamflowusing SWAT with real and simulated meteorological datardquoApplied Geography vol 35 no 1-2 pp 174ndash190 2012

[14] E Lioubimtseva R Cole J M Adams and G KapustinldquoImpacts of climate and land-cover changes in arid lands ofCentral Asiardquo Journal of Arid Environments vol 62 no 2 pp285ndash308 2005

[15] Z Wang D L Ficklin Y Zhang and M Zhang ldquoImpact ofclimate change on streamflow in the arid Shiyang River Basinof northwest Chinardquo Hydrological Processes vol 26 no 18 pp2733ndash2744 2012

[16] D R DrsquoAgostino L G Trisorio N Lamaddalena and R RagabldquoAssessing the results of scenarios of climate and land usechanges on the hydrology of an Italian catchment modellingstudyrdquo Hydrological Processes vol 24 no 19 pp 2693ndash27042010

[17] V Mishra K A Cherkauer D Niyogi et al ldquoA regional scaleassessment of land useland cover and climatic changes onwater and energy cycle in the upper Midwest United StatesrdquoInternational Journal of Climatology vol 30 no 13 pp 2025ndash2044 2010

[18] J A Vano J A Foley C J Kucharik andM T Coe ldquoEvaluatingthe seasonal and interannual variations in water balance inNorthern Wisconsin using a land surface modelrdquo Journal ofGeophysical Research G Biogeosciences vol 111 no 2 2006

[19] D Mao and K A Cherkauer ldquoImpacts of land-use changeon hydrologic responses in the Great Lakes regionrdquo Journal ofHydrology vol 374 no 1-2 pp 71ndash82 2009

Advances in Meteorology 11

[20] J-Y Park M-J Park M-J H-K Joh et al ldquoAssessment ofMIROC32 hires climate and clue-s land use change impacts onwatershed hydrology using Swatrdquo Transactions of the ASABEvol 54 no 5 pp 1713ndash1724 2011

[21] S J Kim H J Kwon G A Park and M S Lee ldquoAssessmentof land-use impact on streamflow via a grid-based modellingapproach including paddy fieldsrdquoHydrological Processes vol 19no 19 pp 3801ndash3817 2005

[22] E Lu E S Takle and J Manoj ldquoThe relationships betweenclimatic and hydrological changes in the upperMississippi riverbasin a SWAT and multi-GCM studyrdquo Journal of Hydrometeo-rology vol 11 no 2 pp 437ndash451 2010

[23] L Liu Z Liu X Ren T Fischer and Y Xu ldquoHydrologicalimpacts of climate change in the Yellow River Basin for the 21stcentury using hydrological model and statistical downscalingmodelrdquo Quaternary International vol 244 no 2 pp 211ndash2202011

[24] S Fall D Niyogi A Gluhovsky R A Pielke E Kalnay andG Rochon ldquoImpacts of land use land cover on temperaturetrends over the continental United States assessment using theNorth American Regional Reanalysisrdquo International Journal ofClimatology vol 30 no 13 pp 1980ndash1993 2010

[25] H Guo Q Hu and T Jiang ldquoAnnual and seasonal streamflowresponses to climate and land-cover changes in the Poyang Lakebasin Chinardquo Journal of Hydrology vol 355 no 1ndash4 pp 106ndash122 2008

[26] L Tang D Yang H Hu and B Gao ldquoDetecting the effect ofland-use change on streamflow sediment and nutrient losses bydistributed hydrological simulationrdquo Journal of Hydrology vol409 no 1-2 pp 172ndash182 2011

[27] M D Tomer and K E Schilling ldquoA simple approach todistinguish land-use and climate-change effects on watershedhydrologyrdquo Journal of Hydrology vol 376 no 1-2 pp 24ndash332009

[28] P F Juckem R J Hunt M P Anderson and D M RobertsonldquoEffects of climate and landmanagement change on streamflowin the driftless area ofWisconsinrdquo Journal of Hydrology vol 355no 1ndash4 pp 123ndash130 2008

[29] Q Feng G D Cheng and K N Endo ldquoTowards sustainabledevelopment of the environmentally degraded River Heihebasin Chinardquo Hydrological Sciences Journal vol 46 no 5 pp647ndash658 2001

[30] G-D Cheng H-L Xiao Z-M Xu J-X Li and M-F LuldquoWater issue and its countermeasure in the inland river basinsofNorthwest Chinamdasha case study inHeiheRiver Basinrdquo Journalof Glaciology and Geocryology vol 28 no 3 pp 406ndash413 2006

[31] A Jarvis H I Reuter A Nelson and E Guevara Hole-FilledSeamless SRTM Data V3 International Centre for TropicalAgriculture (CIAT) 2006

[32] X Deng Q Jiang H Su and F Wu ldquoTrace forest conversionsin Northeast China with a 1-km area percentage data modelrdquoJournal of Applied Remote Sensing vol 4 no 1 Article ID041893 pp 1ndash13 2010

[33] X Deng H Su and J Zhan ldquoIntegration of multiple datasources to simulate the dynamics of land systemsrdquo Sensors vol8 no 2 pp 620ndash634 2008

[34] X Deng J Liu Y Lin and C Shi ldquoA framework for the landuse change dynamics model compatible with rcmsrdquo Advancesin Meteorology vol 2013 Article ID 658941 7 pages 2013

[35] X Deng C Zhao and H Yan ldquoSystematic modeling ofimpacts of land use and land cover changes on regional climate

a reviewrdquo Advances in Meteorology vol 2013 Article ID 31767811 pages 2013

[36] X Deng F Yin Y Lin Q Jin and R Qu ldquoEquilibrium analyseson structural changes of land uses in Jiangxi Provincerdquo Journalof Food Agriculture and Environment vol 10 no 1 pp 846ndash8522012

[37] X Deng Q Jiang J Zhan S He and Y Lin ldquoSimulation on thedynamics of forest area changes in Northeast Chinardquo Journal ofGeographical Sciences vol 20 no 4 pp 495ndash509 2010

[38] X Deng C Zhao Y Lin et al ldquoDownscaling the impactsof large-scale LUCC on surface temperature along with IPCCRCPs a global perspectiverdquoEnergies vol 7 no 4 pp 2720ndash27392014

[39] S Neitsch J Arnold J E A Kiniry R Srinivasan and JWilliams ldquoSoil and water assessment tool userrsquos manual version2000rdquo GSWRL Report 02-06 2002

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EarthquakesJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MineralogyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geological ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geology Advances in

Advances in Meteorology 7

Calibration in 2004

Sim

ulat

ion

(m3s

)

Measurement (m3s)

minus50

0 50 100 150 200 250 300 350

0

50

100

150

200

250

300

350

R2= 085

Ens = 082

(a)

Validation in 2005

Sim

ulat

ion

(m3s

)Measurement (m3

s)0 50 100 150 200 250 300

0

50

100

150

200

250

R2= 081

Ens = 078

(b)

Figure 3 The calibration and validation on streamflow of the SWAT

Trend curve

(∘C)

minus3

minus2

minus1

0

1

2

3

0 50 100 150 200 250 300

(a)

()

minus2

0

2

4

6

8

10

0 50 100 150 200 250 300

(b)

Figure 4 The difference of average monthly temperature and precipitation between 1981ndash2005 and 2006ndash2030 in Qilian Weather observedstation

the probability maps prepared with logistic regression anal-ysis the land demands under different scenarios and themap of development-restricted areas The simulation resultsindicated that the most dramatic land use changes during2006ndash2030 will mainly occur in the upper reach and some

parts of the middle reach of Heihe River Basin Comparedto 2005 the areas of forest land and unused land in 2030 willdecrease by 62 and 16 respectively while the areas ofbuilt-up land cultivated land and grassland will increase by17 13 and 48 respectively (Figure 5) The significant

8 Advances in Meteorology

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E 102

∘E

2000

39∘N

38∘N

39∘N

38∘N

40∘N

(a)

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E

2005

39∘N

38∘N

39∘N

38∘N

(b)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2010

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

38∘N

39∘N

40∘N

38∘N

(c)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2030

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

40∘N

38∘N

39∘N

38∘N

(d)

Figure 5 Comparison of the land uses interpreted in 2000 2005 and simulated in 2010 2030

increase of grassland area may mainly result from the steadypasture construction and this uptrend may continue in thefuture owing to the increasing demand for pasture products

34 Impacts of Climate and Land Use Changes on WatershedHydrology Four simulation experiments were designedbased on the land use data and climate data In the baselineexperiment for the period during 1981ndash2005 the wateryield was simulated with the land use data in 2000 2005and the weather station observations during 1981ndash2005(Figure 6(b)) Then three scenarios for the period during2006ndash2030 were designed based on the land use and climatechange (Figure 6(a)) the results from which were comparedwith that in the baseline experiment In the first scenarioduring 2006ndash2030 the water yield was simulation with theland use data in 2010 and 2030 temperature data during2006ndash2030 and the precipitation data during 1981ndash2005Thesimulation result shows that the impacts of future land use

change on the water yield vary with seasons and the landuse change will have negative overall influence on the wateryield with an influence degree of minus18 according to theannual mean water yield

The second scenario during 2006ndash2030 was based onscenarios of temperature and land use changes The secondexperiment used the land use data in 2010 and 2030 scenariodata of temperature during 2006ndash2030 and precipitation dataduring 1981ndash2005 The analysis of climate change scenariosshows that the average temperature will rise by 08∘C between1981ndash2005 and 2006ndash2030 The simulation result in thesecond experiment shows that the land use and temperaturechanges will make the water yield change by 06ndash11 thechange range of which is relatively smaller compared to thesimulation results under the scenario with only land usechange The reasons may be that the temperature rise andmelting of a small amount of snow slightly offset the adverseeffects of land use change At the same time the higher

Advances in Meteorology 9

Case ICase II

Case III

minus80

minus60

minus40

minus20

0

20

40

60

80

()

2006

12

1

2007

12

1

2008

12

1

2009

12

1

2010

12

1

2011

12

1

2012

12

1

2013

12

1

2014

12

1

2015

12

1

2016

12

1

2017

12

1

2018

12

1

2019

12

1

2020

12

1

2021

12

1

2022

12

1

2023

12

1

2024

12

1

2025

12

1

2026

12

1

2027

12

1

2028

12

1

(a)

1981

12

1

1982

12

1

1983

12

1

1984

12

1

1985

12

1

1986

12

1

1987

12

1

1988

12

1

1989

12

1

1990

12

1

1991

12

1

1992

12

1

1993

12

1

1994

12

1

1995

12

1

1996

12

1

1997

12

1

1998

12

1

1999

12

1

2000

12

1

2001

12

1

2002

12

1

2003

12

1

(mm

)

Simulated water yield during 1981ndash2005

40

35

30

25

20

15

10

5

0

(b)

Figure 6 Comparison of water yields under three scenarios

temperatures will result in more winter precipitation in theform of rain rather than snow leading to the hydrologicconsequences including increased winter discharge a shiftin the spring snowmelt peak to earlier in the season anddecreased summer discharge

The third scenario during 2006ndash2030 involves scenariosof changes in all of land use temperature and precipitationThe land use data in 2010 and 2030 and temperature andprecipitation data during 2006ndash2030 were used under thethird scenario The simulation result shows that these threefactors jointly have positive impacts on the water yieldmaking the basin water yield increase by about 98 Theincrease of the basin water yield is mainly caused by thechange in precipitation which will increase by around 108during 2006ndash2030 in comparison to that during 1981ndash2005Overall the simulation results suggest that the basin wateryield will increase in the future under different scenarios ofclimate and land use changes

4 Discussion and Conclusions

In the Heihe River Basin the upper reaches are featuredwith the generation and use of blue water while the lowerreach and surrounding areas are characterized by naturalecosystems and a low population density LULC is defined assyndromes of human activities such as agriculture forestryand building construction and most of previous studies onlyfocused on the hydrological influence of LULC change in theupper reachThe separation between hydrological impacts ofland use and climate changes has never been studied in theupper andmiddle reaches of the Heihe River Basin Howeverwe argue that studying the hydrological processes in theupper and middle reaches is essential since water supply tothe lower reach is impacted by both the climate change andhuman activities in the upper and middle reaches

In this study we analyzed the impacts of potential climateand land use changes on the water yield in the upper and

10 Advances in Meteorology

middle reaches of Heihe River Basin based on the simulationwith the SWAT model The results show that the water yieldwas more affected by climate change than by land use changeThis indicates that the predicted increase in precipitation willexert more significant impacts on the watershed hydrologythan the predicted land use changes will However the anal-ysis of the projected streamflow changes shows that there arehigher uncertainties in the dry season compared with thewet season in the simulation with the hydrological modeland GCMs climate data It is difficult to accurately projectthe hydrological changes since there are various uncertaintiesassociated with the future Green House Gas (GHG) emissionscenarios GCM structure downscaling method LULC andhydrological models In particular water resource managersare generally confronted with complex problems in sustain-able management and conservation of water resources dueto the uncertainties in the future hydrological projectionunder climate and land use changes It is therefore crucial toconsider both land use and climate changes in water resourceplanning for the Heihe River Basin so as to mitigate theirnegative hydrological impacts and more valuable informa-tion may be provided to the water resource managers if theseuncertainties in the future hydrological projection can beeffectively reduced through advancedmodeling and research

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was financially supported by themajor researchplan of the National Natural Science Foundation of China(Grant no 91325302) National Basic Research Program ofChina (973 Program) (no 2010CB950904) and the NationalNatural Science Funds of China for Distinguished YoungScholar (Grant no 71225005)

References

[1] Z ZaludM TrnkaMDubrovsky P Hlavinka D Semeradovaand E Kocmankova ldquoClimate change impacts on selectedaspects of the Czech agricultural productionrdquo Plant ProtectionScience vol 45 pp S11ndashS19 2009

[2] E Lorencova J Frelichova E Nelson and D Vackar ldquoPast andfuture impacts of land use and climate change on agriculturalecosystem services in the Czech Republicrdquo Land Use Policy vol33 pp 183ndash194 2013

[3] RMahmood A I Quintanar G Conner et al ldquoImpacts of landuseland cover change on climate and future research prioritiesrdquoBulletin of the AmericanMeteorological Society vol 91 no 1 pp37ndash46 2010

[4] Z Ma S Kang L Zhang L Tong and X Su ldquoAnalysis ofimpacts of climate variability and human activity on streamflowfor a river basin in arid region of northwest Chinardquo Journal ofHydrology vol 352 no 3-4 pp 239ndash249 2008

[5] B R Scanlon I Jolly M Sophocleous and L Zhang ldquoGlobalimpacts of conversions from natural to agricultural ecosystems

on water resources quantity versus qualityrdquo Water ResourcesResearch vol 43 no 3 Article ID W03437 2007

[6] L M Mango A M Melesse M E McClain D Gann and S GSetegn ldquoLand use and climate change impacts on the hydrologyof the upper Mara River Basin Kenya results of a modelingstudy to support better resource managementrdquo Hydrology andEarth System Sciences vol 15 no 7 pp 2245ndash2258 2011

[7] C J Vorosmarty P Green J Salisbury and R B LammersldquoGlobal water resources vulnerability from climate change andpopulation growthrdquo Science vol 289 no 5477 pp 284ndash2882000

[8] S Qi G Sun Y Wang S G McNulty and J A M MyersldquoStreamflow response to climate and landuse changes in acoastal watershed in North Carolinardquo Transactions of theASABE vol 52 no 3 pp 739ndash749 2009

[9] J Kim J Choi C Choi and S Park ldquoImpacts of changes inclimate and land useland cover under IPCC RCP scenarios onstreamflow in theHoeya River Basin Koreardquo Science of the TotalEnvironment vol 452-453 pp 181ndash195 2013

[10] Z Li W-Z Liu X-C Zhang and F-L Zheng ldquoImpacts ofland use change and climate variability on hydrology in anagricultural catchment on the Loess Plateau of Chinardquo Journalof Hydrology vol 377 no 1-2 pp 35ndash42 2009

[11] R W Skaggs D M Amatya G Chescheir C Blanton andJ Gilliam ldquoEffect of drainage and management practices onhydrology of pine plantationrdquo in Proceedings of the InternationalConference onHydrology andManagement of ForestedWetlands2006

[12] T D Prowse S Beltaos J T Gardner et al ldquoClimate changeflow regulation and land-use effects on the hydrology of thePeace-Athabasca-Slave system Findings from the NorthernRivers Ecosystem Initiativerdquo Environmental Monitoring andAssessment vol 113 no 1ndash3 pp 167ndash197 2006

[13] B Dixon and J Earls ldquoEffects of urbanization on streamflowusing SWAT with real and simulated meteorological datardquoApplied Geography vol 35 no 1-2 pp 174ndash190 2012

[14] E Lioubimtseva R Cole J M Adams and G KapustinldquoImpacts of climate and land-cover changes in arid lands ofCentral Asiardquo Journal of Arid Environments vol 62 no 2 pp285ndash308 2005

[15] Z Wang D L Ficklin Y Zhang and M Zhang ldquoImpact ofclimate change on streamflow in the arid Shiyang River Basinof northwest Chinardquo Hydrological Processes vol 26 no 18 pp2733ndash2744 2012

[16] D R DrsquoAgostino L G Trisorio N Lamaddalena and R RagabldquoAssessing the results of scenarios of climate and land usechanges on the hydrology of an Italian catchment modellingstudyrdquo Hydrological Processes vol 24 no 19 pp 2693ndash27042010

[17] V Mishra K A Cherkauer D Niyogi et al ldquoA regional scaleassessment of land useland cover and climatic changes onwater and energy cycle in the upper Midwest United StatesrdquoInternational Journal of Climatology vol 30 no 13 pp 2025ndash2044 2010

[18] J A Vano J A Foley C J Kucharik andM T Coe ldquoEvaluatingthe seasonal and interannual variations in water balance inNorthern Wisconsin using a land surface modelrdquo Journal ofGeophysical Research G Biogeosciences vol 111 no 2 2006

[19] D Mao and K A Cherkauer ldquoImpacts of land-use changeon hydrologic responses in the Great Lakes regionrdquo Journal ofHydrology vol 374 no 1-2 pp 71ndash82 2009

Advances in Meteorology 11

[20] J-Y Park M-J Park M-J H-K Joh et al ldquoAssessment ofMIROC32 hires climate and clue-s land use change impacts onwatershed hydrology using Swatrdquo Transactions of the ASABEvol 54 no 5 pp 1713ndash1724 2011

[21] S J Kim H J Kwon G A Park and M S Lee ldquoAssessmentof land-use impact on streamflow via a grid-based modellingapproach including paddy fieldsrdquoHydrological Processes vol 19no 19 pp 3801ndash3817 2005

[22] E Lu E S Takle and J Manoj ldquoThe relationships betweenclimatic and hydrological changes in the upperMississippi riverbasin a SWAT and multi-GCM studyrdquo Journal of Hydrometeo-rology vol 11 no 2 pp 437ndash451 2010

[23] L Liu Z Liu X Ren T Fischer and Y Xu ldquoHydrologicalimpacts of climate change in the Yellow River Basin for the 21stcentury using hydrological model and statistical downscalingmodelrdquo Quaternary International vol 244 no 2 pp 211ndash2202011

[24] S Fall D Niyogi A Gluhovsky R A Pielke E Kalnay andG Rochon ldquoImpacts of land use land cover on temperaturetrends over the continental United States assessment using theNorth American Regional Reanalysisrdquo International Journal ofClimatology vol 30 no 13 pp 1980ndash1993 2010

[25] H Guo Q Hu and T Jiang ldquoAnnual and seasonal streamflowresponses to climate and land-cover changes in the Poyang Lakebasin Chinardquo Journal of Hydrology vol 355 no 1ndash4 pp 106ndash122 2008

[26] L Tang D Yang H Hu and B Gao ldquoDetecting the effect ofland-use change on streamflow sediment and nutrient losses bydistributed hydrological simulationrdquo Journal of Hydrology vol409 no 1-2 pp 172ndash182 2011

[27] M D Tomer and K E Schilling ldquoA simple approach todistinguish land-use and climate-change effects on watershedhydrologyrdquo Journal of Hydrology vol 376 no 1-2 pp 24ndash332009

[28] P F Juckem R J Hunt M P Anderson and D M RobertsonldquoEffects of climate and landmanagement change on streamflowin the driftless area ofWisconsinrdquo Journal of Hydrology vol 355no 1ndash4 pp 123ndash130 2008

[29] Q Feng G D Cheng and K N Endo ldquoTowards sustainabledevelopment of the environmentally degraded River Heihebasin Chinardquo Hydrological Sciences Journal vol 46 no 5 pp647ndash658 2001

[30] G-D Cheng H-L Xiao Z-M Xu J-X Li and M-F LuldquoWater issue and its countermeasure in the inland river basinsofNorthwest Chinamdasha case study inHeiheRiver Basinrdquo Journalof Glaciology and Geocryology vol 28 no 3 pp 406ndash413 2006

[31] A Jarvis H I Reuter A Nelson and E Guevara Hole-FilledSeamless SRTM Data V3 International Centre for TropicalAgriculture (CIAT) 2006

[32] X Deng Q Jiang H Su and F Wu ldquoTrace forest conversionsin Northeast China with a 1-km area percentage data modelrdquoJournal of Applied Remote Sensing vol 4 no 1 Article ID041893 pp 1ndash13 2010

[33] X Deng H Su and J Zhan ldquoIntegration of multiple datasources to simulate the dynamics of land systemsrdquo Sensors vol8 no 2 pp 620ndash634 2008

[34] X Deng J Liu Y Lin and C Shi ldquoA framework for the landuse change dynamics model compatible with rcmsrdquo Advancesin Meteorology vol 2013 Article ID 658941 7 pages 2013

[35] X Deng C Zhao and H Yan ldquoSystematic modeling ofimpacts of land use and land cover changes on regional climate

a reviewrdquo Advances in Meteorology vol 2013 Article ID 31767811 pages 2013

[36] X Deng F Yin Y Lin Q Jin and R Qu ldquoEquilibrium analyseson structural changes of land uses in Jiangxi Provincerdquo Journalof Food Agriculture and Environment vol 10 no 1 pp 846ndash8522012

[37] X Deng Q Jiang J Zhan S He and Y Lin ldquoSimulation on thedynamics of forest area changes in Northeast Chinardquo Journal ofGeographical Sciences vol 20 no 4 pp 495ndash509 2010

[38] X Deng C Zhao Y Lin et al ldquoDownscaling the impactsof large-scale LUCC on surface temperature along with IPCCRCPs a global perspectiverdquoEnergies vol 7 no 4 pp 2720ndash27392014

[39] S Neitsch J Arnold J E A Kiniry R Srinivasan and JWilliams ldquoSoil and water assessment tool userrsquos manual version2000rdquo GSWRL Report 02-06 2002

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EarthquakesJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MineralogyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geological ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geology Advances in

8 Advances in Meteorology

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E 102

∘E

2000

39∘N

38∘N

39∘N

38∘N

40∘N

(a)

99∘E 100

∘E 101∘E 102

∘E

99∘E 100

∘E 101∘E

2005

39∘N

38∘N

39∘N

38∘N

(b)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2010

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

38∘N

39∘N

40∘N

38∘N

(c)

98∘E 99

∘E 100∘E 101

∘E 102∘E

99∘E 100

∘E 101∘E 102

∘E

2030

Cultivated land

Forest

GrasslandWater area

Built-up area

Unused land

River

39∘N

40∘N

38∘N

39∘N

38∘N

(d)

Figure 5 Comparison of the land uses interpreted in 2000 2005 and simulated in 2010 2030

increase of grassland area may mainly result from the steadypasture construction and this uptrend may continue in thefuture owing to the increasing demand for pasture products

34 Impacts of Climate and Land Use Changes on WatershedHydrology Four simulation experiments were designedbased on the land use data and climate data In the baselineexperiment for the period during 1981ndash2005 the wateryield was simulated with the land use data in 2000 2005and the weather station observations during 1981ndash2005(Figure 6(b)) Then three scenarios for the period during2006ndash2030 were designed based on the land use and climatechange (Figure 6(a)) the results from which were comparedwith that in the baseline experiment In the first scenarioduring 2006ndash2030 the water yield was simulation with theland use data in 2010 and 2030 temperature data during2006ndash2030 and the precipitation data during 1981ndash2005Thesimulation result shows that the impacts of future land use

change on the water yield vary with seasons and the landuse change will have negative overall influence on the wateryield with an influence degree of minus18 according to theannual mean water yield

The second scenario during 2006ndash2030 was based onscenarios of temperature and land use changes The secondexperiment used the land use data in 2010 and 2030 scenariodata of temperature during 2006ndash2030 and precipitation dataduring 1981ndash2005 The analysis of climate change scenariosshows that the average temperature will rise by 08∘C between1981ndash2005 and 2006ndash2030 The simulation result in thesecond experiment shows that the land use and temperaturechanges will make the water yield change by 06ndash11 thechange range of which is relatively smaller compared to thesimulation results under the scenario with only land usechange The reasons may be that the temperature rise andmelting of a small amount of snow slightly offset the adverseeffects of land use change At the same time the higher

Advances in Meteorology 9

Case ICase II

Case III

minus80

minus60

minus40

minus20

0

20

40

60

80

()

2006

12

1

2007

12

1

2008

12

1

2009

12

1

2010

12

1

2011

12

1

2012

12

1

2013

12

1

2014

12

1

2015

12

1

2016

12

1

2017

12

1

2018

12

1

2019

12

1

2020

12

1

2021

12

1

2022

12

1

2023

12

1

2024

12

1

2025

12

1

2026

12

1

2027

12

1

2028

12

1

(a)

1981

12

1

1982

12

1

1983

12

1

1984

12

1

1985

12

1

1986

12

1

1987

12

1

1988

12

1

1989

12

1

1990

12

1

1991

12

1

1992

12

1

1993

12

1

1994

12

1

1995

12

1

1996

12

1

1997

12

1

1998

12

1

1999

12

1

2000

12

1

2001

12

1

2002

12

1

2003

12

1

(mm

)

Simulated water yield during 1981ndash2005

40

35

30

25

20

15

10

5

0

(b)

Figure 6 Comparison of water yields under three scenarios

temperatures will result in more winter precipitation in theform of rain rather than snow leading to the hydrologicconsequences including increased winter discharge a shiftin the spring snowmelt peak to earlier in the season anddecreased summer discharge

The third scenario during 2006ndash2030 involves scenariosof changes in all of land use temperature and precipitationThe land use data in 2010 and 2030 and temperature andprecipitation data during 2006ndash2030 were used under thethird scenario The simulation result shows that these threefactors jointly have positive impacts on the water yieldmaking the basin water yield increase by about 98 Theincrease of the basin water yield is mainly caused by thechange in precipitation which will increase by around 108during 2006ndash2030 in comparison to that during 1981ndash2005Overall the simulation results suggest that the basin wateryield will increase in the future under different scenarios ofclimate and land use changes

4 Discussion and Conclusions

In the Heihe River Basin the upper reaches are featuredwith the generation and use of blue water while the lowerreach and surrounding areas are characterized by naturalecosystems and a low population density LULC is defined assyndromes of human activities such as agriculture forestryand building construction and most of previous studies onlyfocused on the hydrological influence of LULC change in theupper reachThe separation between hydrological impacts ofland use and climate changes has never been studied in theupper andmiddle reaches of the Heihe River Basin Howeverwe argue that studying the hydrological processes in theupper and middle reaches is essential since water supply tothe lower reach is impacted by both the climate change andhuman activities in the upper and middle reaches

In this study we analyzed the impacts of potential climateand land use changes on the water yield in the upper and

10 Advances in Meteorology

middle reaches of Heihe River Basin based on the simulationwith the SWAT model The results show that the water yieldwas more affected by climate change than by land use changeThis indicates that the predicted increase in precipitation willexert more significant impacts on the watershed hydrologythan the predicted land use changes will However the anal-ysis of the projected streamflow changes shows that there arehigher uncertainties in the dry season compared with thewet season in the simulation with the hydrological modeland GCMs climate data It is difficult to accurately projectthe hydrological changes since there are various uncertaintiesassociated with the future Green House Gas (GHG) emissionscenarios GCM structure downscaling method LULC andhydrological models In particular water resource managersare generally confronted with complex problems in sustain-able management and conservation of water resources dueto the uncertainties in the future hydrological projectionunder climate and land use changes It is therefore crucial toconsider both land use and climate changes in water resourceplanning for the Heihe River Basin so as to mitigate theirnegative hydrological impacts and more valuable informa-tion may be provided to the water resource managers if theseuncertainties in the future hydrological projection can beeffectively reduced through advancedmodeling and research

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was financially supported by themajor researchplan of the National Natural Science Foundation of China(Grant no 91325302) National Basic Research Program ofChina (973 Program) (no 2010CB950904) and the NationalNatural Science Funds of China for Distinguished YoungScholar (Grant no 71225005)

References

[1] Z ZaludM TrnkaMDubrovsky P Hlavinka D Semeradovaand E Kocmankova ldquoClimate change impacts on selectedaspects of the Czech agricultural productionrdquo Plant ProtectionScience vol 45 pp S11ndashS19 2009

[2] E Lorencova J Frelichova E Nelson and D Vackar ldquoPast andfuture impacts of land use and climate change on agriculturalecosystem services in the Czech Republicrdquo Land Use Policy vol33 pp 183ndash194 2013

[3] RMahmood A I Quintanar G Conner et al ldquoImpacts of landuseland cover change on climate and future research prioritiesrdquoBulletin of the AmericanMeteorological Society vol 91 no 1 pp37ndash46 2010

[4] Z Ma S Kang L Zhang L Tong and X Su ldquoAnalysis ofimpacts of climate variability and human activity on streamflowfor a river basin in arid region of northwest Chinardquo Journal ofHydrology vol 352 no 3-4 pp 239ndash249 2008

[5] B R Scanlon I Jolly M Sophocleous and L Zhang ldquoGlobalimpacts of conversions from natural to agricultural ecosystems

on water resources quantity versus qualityrdquo Water ResourcesResearch vol 43 no 3 Article ID W03437 2007

[6] L M Mango A M Melesse M E McClain D Gann and S GSetegn ldquoLand use and climate change impacts on the hydrologyof the upper Mara River Basin Kenya results of a modelingstudy to support better resource managementrdquo Hydrology andEarth System Sciences vol 15 no 7 pp 2245ndash2258 2011

[7] C J Vorosmarty P Green J Salisbury and R B LammersldquoGlobal water resources vulnerability from climate change andpopulation growthrdquo Science vol 289 no 5477 pp 284ndash2882000

[8] S Qi G Sun Y Wang S G McNulty and J A M MyersldquoStreamflow response to climate and landuse changes in acoastal watershed in North Carolinardquo Transactions of theASABE vol 52 no 3 pp 739ndash749 2009

[9] J Kim J Choi C Choi and S Park ldquoImpacts of changes inclimate and land useland cover under IPCC RCP scenarios onstreamflow in theHoeya River Basin Koreardquo Science of the TotalEnvironment vol 452-453 pp 181ndash195 2013

[10] Z Li W-Z Liu X-C Zhang and F-L Zheng ldquoImpacts ofland use change and climate variability on hydrology in anagricultural catchment on the Loess Plateau of Chinardquo Journalof Hydrology vol 377 no 1-2 pp 35ndash42 2009

[11] R W Skaggs D M Amatya G Chescheir C Blanton andJ Gilliam ldquoEffect of drainage and management practices onhydrology of pine plantationrdquo in Proceedings of the InternationalConference onHydrology andManagement of ForestedWetlands2006

[12] T D Prowse S Beltaos J T Gardner et al ldquoClimate changeflow regulation and land-use effects on the hydrology of thePeace-Athabasca-Slave system Findings from the NorthernRivers Ecosystem Initiativerdquo Environmental Monitoring andAssessment vol 113 no 1ndash3 pp 167ndash197 2006

[13] B Dixon and J Earls ldquoEffects of urbanization on streamflowusing SWAT with real and simulated meteorological datardquoApplied Geography vol 35 no 1-2 pp 174ndash190 2012

[14] E Lioubimtseva R Cole J M Adams and G KapustinldquoImpacts of climate and land-cover changes in arid lands ofCentral Asiardquo Journal of Arid Environments vol 62 no 2 pp285ndash308 2005

[15] Z Wang D L Ficklin Y Zhang and M Zhang ldquoImpact ofclimate change on streamflow in the arid Shiyang River Basinof northwest Chinardquo Hydrological Processes vol 26 no 18 pp2733ndash2744 2012

[16] D R DrsquoAgostino L G Trisorio N Lamaddalena and R RagabldquoAssessing the results of scenarios of climate and land usechanges on the hydrology of an Italian catchment modellingstudyrdquo Hydrological Processes vol 24 no 19 pp 2693ndash27042010

[17] V Mishra K A Cherkauer D Niyogi et al ldquoA regional scaleassessment of land useland cover and climatic changes onwater and energy cycle in the upper Midwest United StatesrdquoInternational Journal of Climatology vol 30 no 13 pp 2025ndash2044 2010

[18] J A Vano J A Foley C J Kucharik andM T Coe ldquoEvaluatingthe seasonal and interannual variations in water balance inNorthern Wisconsin using a land surface modelrdquo Journal ofGeophysical Research G Biogeosciences vol 111 no 2 2006

[19] D Mao and K A Cherkauer ldquoImpacts of land-use changeon hydrologic responses in the Great Lakes regionrdquo Journal ofHydrology vol 374 no 1-2 pp 71ndash82 2009

Advances in Meteorology 11

[20] J-Y Park M-J Park M-J H-K Joh et al ldquoAssessment ofMIROC32 hires climate and clue-s land use change impacts onwatershed hydrology using Swatrdquo Transactions of the ASABEvol 54 no 5 pp 1713ndash1724 2011

[21] S J Kim H J Kwon G A Park and M S Lee ldquoAssessmentof land-use impact on streamflow via a grid-based modellingapproach including paddy fieldsrdquoHydrological Processes vol 19no 19 pp 3801ndash3817 2005

[22] E Lu E S Takle and J Manoj ldquoThe relationships betweenclimatic and hydrological changes in the upperMississippi riverbasin a SWAT and multi-GCM studyrdquo Journal of Hydrometeo-rology vol 11 no 2 pp 437ndash451 2010

[23] L Liu Z Liu X Ren T Fischer and Y Xu ldquoHydrologicalimpacts of climate change in the Yellow River Basin for the 21stcentury using hydrological model and statistical downscalingmodelrdquo Quaternary International vol 244 no 2 pp 211ndash2202011

[24] S Fall D Niyogi A Gluhovsky R A Pielke E Kalnay andG Rochon ldquoImpacts of land use land cover on temperaturetrends over the continental United States assessment using theNorth American Regional Reanalysisrdquo International Journal ofClimatology vol 30 no 13 pp 1980ndash1993 2010

[25] H Guo Q Hu and T Jiang ldquoAnnual and seasonal streamflowresponses to climate and land-cover changes in the Poyang Lakebasin Chinardquo Journal of Hydrology vol 355 no 1ndash4 pp 106ndash122 2008

[26] L Tang D Yang H Hu and B Gao ldquoDetecting the effect ofland-use change on streamflow sediment and nutrient losses bydistributed hydrological simulationrdquo Journal of Hydrology vol409 no 1-2 pp 172ndash182 2011

[27] M D Tomer and K E Schilling ldquoA simple approach todistinguish land-use and climate-change effects on watershedhydrologyrdquo Journal of Hydrology vol 376 no 1-2 pp 24ndash332009

[28] P F Juckem R J Hunt M P Anderson and D M RobertsonldquoEffects of climate and landmanagement change on streamflowin the driftless area ofWisconsinrdquo Journal of Hydrology vol 355no 1ndash4 pp 123ndash130 2008

[29] Q Feng G D Cheng and K N Endo ldquoTowards sustainabledevelopment of the environmentally degraded River Heihebasin Chinardquo Hydrological Sciences Journal vol 46 no 5 pp647ndash658 2001

[30] G-D Cheng H-L Xiao Z-M Xu J-X Li and M-F LuldquoWater issue and its countermeasure in the inland river basinsofNorthwest Chinamdasha case study inHeiheRiver Basinrdquo Journalof Glaciology and Geocryology vol 28 no 3 pp 406ndash413 2006

[31] A Jarvis H I Reuter A Nelson and E Guevara Hole-FilledSeamless SRTM Data V3 International Centre for TropicalAgriculture (CIAT) 2006

[32] X Deng Q Jiang H Su and F Wu ldquoTrace forest conversionsin Northeast China with a 1-km area percentage data modelrdquoJournal of Applied Remote Sensing vol 4 no 1 Article ID041893 pp 1ndash13 2010

[33] X Deng H Su and J Zhan ldquoIntegration of multiple datasources to simulate the dynamics of land systemsrdquo Sensors vol8 no 2 pp 620ndash634 2008

[34] X Deng J Liu Y Lin and C Shi ldquoA framework for the landuse change dynamics model compatible with rcmsrdquo Advancesin Meteorology vol 2013 Article ID 658941 7 pages 2013

[35] X Deng C Zhao and H Yan ldquoSystematic modeling ofimpacts of land use and land cover changes on regional climate

a reviewrdquo Advances in Meteorology vol 2013 Article ID 31767811 pages 2013

[36] X Deng F Yin Y Lin Q Jin and R Qu ldquoEquilibrium analyseson structural changes of land uses in Jiangxi Provincerdquo Journalof Food Agriculture and Environment vol 10 no 1 pp 846ndash8522012

[37] X Deng Q Jiang J Zhan S He and Y Lin ldquoSimulation on thedynamics of forest area changes in Northeast Chinardquo Journal ofGeographical Sciences vol 20 no 4 pp 495ndash509 2010

[38] X Deng C Zhao Y Lin et al ldquoDownscaling the impactsof large-scale LUCC on surface temperature along with IPCCRCPs a global perspectiverdquoEnergies vol 7 no 4 pp 2720ndash27392014

[39] S Neitsch J Arnold J E A Kiniry R Srinivasan and JWilliams ldquoSoil and water assessment tool userrsquos manual version2000rdquo GSWRL Report 02-06 2002

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EarthquakesJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MineralogyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geological ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geology Advances in

Advances in Meteorology 9

Case ICase II

Case III

minus80

minus60

minus40

minus20

0

20

40

60

80

()

2006

12

1

2007

12

1

2008

12

1

2009

12

1

2010

12

1

2011

12

1

2012

12

1

2013

12

1

2014

12

1

2015

12

1

2016

12

1

2017

12

1

2018

12

1

2019

12

1

2020

12

1

2021

12

1

2022

12

1

2023

12

1

2024

12

1

2025

12

1

2026

12

1

2027

12

1

2028

12

1

(a)

1981

12

1

1982

12

1

1983

12

1

1984

12

1

1985

12

1

1986

12

1

1987

12

1

1988

12

1

1989

12

1

1990

12

1

1991

12

1

1992

12

1

1993

12

1

1994

12

1

1995

12

1

1996

12

1

1997

12

1

1998

12

1

1999

12

1

2000

12

1

2001

12

1

2002

12

1

2003

12

1

(mm

)

Simulated water yield during 1981ndash2005

40

35

30

25

20

15

10

5

0

(b)

Figure 6 Comparison of water yields under three scenarios

temperatures will result in more winter precipitation in theform of rain rather than snow leading to the hydrologicconsequences including increased winter discharge a shiftin the spring snowmelt peak to earlier in the season anddecreased summer discharge

The third scenario during 2006ndash2030 involves scenariosof changes in all of land use temperature and precipitationThe land use data in 2010 and 2030 and temperature andprecipitation data during 2006ndash2030 were used under thethird scenario The simulation result shows that these threefactors jointly have positive impacts on the water yieldmaking the basin water yield increase by about 98 Theincrease of the basin water yield is mainly caused by thechange in precipitation which will increase by around 108during 2006ndash2030 in comparison to that during 1981ndash2005Overall the simulation results suggest that the basin wateryield will increase in the future under different scenarios ofclimate and land use changes

4 Discussion and Conclusions

In the Heihe River Basin the upper reaches are featuredwith the generation and use of blue water while the lowerreach and surrounding areas are characterized by naturalecosystems and a low population density LULC is defined assyndromes of human activities such as agriculture forestryand building construction and most of previous studies onlyfocused on the hydrological influence of LULC change in theupper reachThe separation between hydrological impacts ofland use and climate changes has never been studied in theupper andmiddle reaches of the Heihe River Basin Howeverwe argue that studying the hydrological processes in theupper and middle reaches is essential since water supply tothe lower reach is impacted by both the climate change andhuman activities in the upper and middle reaches

In this study we analyzed the impacts of potential climateand land use changes on the water yield in the upper and

10 Advances in Meteorology

middle reaches of Heihe River Basin based on the simulationwith the SWAT model The results show that the water yieldwas more affected by climate change than by land use changeThis indicates that the predicted increase in precipitation willexert more significant impacts on the watershed hydrologythan the predicted land use changes will However the anal-ysis of the projected streamflow changes shows that there arehigher uncertainties in the dry season compared with thewet season in the simulation with the hydrological modeland GCMs climate data It is difficult to accurately projectthe hydrological changes since there are various uncertaintiesassociated with the future Green House Gas (GHG) emissionscenarios GCM structure downscaling method LULC andhydrological models In particular water resource managersare generally confronted with complex problems in sustain-able management and conservation of water resources dueto the uncertainties in the future hydrological projectionunder climate and land use changes It is therefore crucial toconsider both land use and climate changes in water resourceplanning for the Heihe River Basin so as to mitigate theirnegative hydrological impacts and more valuable informa-tion may be provided to the water resource managers if theseuncertainties in the future hydrological projection can beeffectively reduced through advancedmodeling and research

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was financially supported by themajor researchplan of the National Natural Science Foundation of China(Grant no 91325302) National Basic Research Program ofChina (973 Program) (no 2010CB950904) and the NationalNatural Science Funds of China for Distinguished YoungScholar (Grant no 71225005)

References

[1] Z ZaludM TrnkaMDubrovsky P Hlavinka D Semeradovaand E Kocmankova ldquoClimate change impacts on selectedaspects of the Czech agricultural productionrdquo Plant ProtectionScience vol 45 pp S11ndashS19 2009

[2] E Lorencova J Frelichova E Nelson and D Vackar ldquoPast andfuture impacts of land use and climate change on agriculturalecosystem services in the Czech Republicrdquo Land Use Policy vol33 pp 183ndash194 2013

[3] RMahmood A I Quintanar G Conner et al ldquoImpacts of landuseland cover change on climate and future research prioritiesrdquoBulletin of the AmericanMeteorological Society vol 91 no 1 pp37ndash46 2010

[4] Z Ma S Kang L Zhang L Tong and X Su ldquoAnalysis ofimpacts of climate variability and human activity on streamflowfor a river basin in arid region of northwest Chinardquo Journal ofHydrology vol 352 no 3-4 pp 239ndash249 2008

[5] B R Scanlon I Jolly M Sophocleous and L Zhang ldquoGlobalimpacts of conversions from natural to agricultural ecosystems

on water resources quantity versus qualityrdquo Water ResourcesResearch vol 43 no 3 Article ID W03437 2007

[6] L M Mango A M Melesse M E McClain D Gann and S GSetegn ldquoLand use and climate change impacts on the hydrologyof the upper Mara River Basin Kenya results of a modelingstudy to support better resource managementrdquo Hydrology andEarth System Sciences vol 15 no 7 pp 2245ndash2258 2011

[7] C J Vorosmarty P Green J Salisbury and R B LammersldquoGlobal water resources vulnerability from climate change andpopulation growthrdquo Science vol 289 no 5477 pp 284ndash2882000

[8] S Qi G Sun Y Wang S G McNulty and J A M MyersldquoStreamflow response to climate and landuse changes in acoastal watershed in North Carolinardquo Transactions of theASABE vol 52 no 3 pp 739ndash749 2009

[9] J Kim J Choi C Choi and S Park ldquoImpacts of changes inclimate and land useland cover under IPCC RCP scenarios onstreamflow in theHoeya River Basin Koreardquo Science of the TotalEnvironment vol 452-453 pp 181ndash195 2013

[10] Z Li W-Z Liu X-C Zhang and F-L Zheng ldquoImpacts ofland use change and climate variability on hydrology in anagricultural catchment on the Loess Plateau of Chinardquo Journalof Hydrology vol 377 no 1-2 pp 35ndash42 2009

[11] R W Skaggs D M Amatya G Chescheir C Blanton andJ Gilliam ldquoEffect of drainage and management practices onhydrology of pine plantationrdquo in Proceedings of the InternationalConference onHydrology andManagement of ForestedWetlands2006

[12] T D Prowse S Beltaos J T Gardner et al ldquoClimate changeflow regulation and land-use effects on the hydrology of thePeace-Athabasca-Slave system Findings from the NorthernRivers Ecosystem Initiativerdquo Environmental Monitoring andAssessment vol 113 no 1ndash3 pp 167ndash197 2006

[13] B Dixon and J Earls ldquoEffects of urbanization on streamflowusing SWAT with real and simulated meteorological datardquoApplied Geography vol 35 no 1-2 pp 174ndash190 2012

[14] E Lioubimtseva R Cole J M Adams and G KapustinldquoImpacts of climate and land-cover changes in arid lands ofCentral Asiardquo Journal of Arid Environments vol 62 no 2 pp285ndash308 2005

[15] Z Wang D L Ficklin Y Zhang and M Zhang ldquoImpact ofclimate change on streamflow in the arid Shiyang River Basinof northwest Chinardquo Hydrological Processes vol 26 no 18 pp2733ndash2744 2012

[16] D R DrsquoAgostino L G Trisorio N Lamaddalena and R RagabldquoAssessing the results of scenarios of climate and land usechanges on the hydrology of an Italian catchment modellingstudyrdquo Hydrological Processes vol 24 no 19 pp 2693ndash27042010

[17] V Mishra K A Cherkauer D Niyogi et al ldquoA regional scaleassessment of land useland cover and climatic changes onwater and energy cycle in the upper Midwest United StatesrdquoInternational Journal of Climatology vol 30 no 13 pp 2025ndash2044 2010

[18] J A Vano J A Foley C J Kucharik andM T Coe ldquoEvaluatingthe seasonal and interannual variations in water balance inNorthern Wisconsin using a land surface modelrdquo Journal ofGeophysical Research G Biogeosciences vol 111 no 2 2006

[19] D Mao and K A Cherkauer ldquoImpacts of land-use changeon hydrologic responses in the Great Lakes regionrdquo Journal ofHydrology vol 374 no 1-2 pp 71ndash82 2009

Advances in Meteorology 11

[20] J-Y Park M-J Park M-J H-K Joh et al ldquoAssessment ofMIROC32 hires climate and clue-s land use change impacts onwatershed hydrology using Swatrdquo Transactions of the ASABEvol 54 no 5 pp 1713ndash1724 2011

[21] S J Kim H J Kwon G A Park and M S Lee ldquoAssessmentof land-use impact on streamflow via a grid-based modellingapproach including paddy fieldsrdquoHydrological Processes vol 19no 19 pp 3801ndash3817 2005

[22] E Lu E S Takle and J Manoj ldquoThe relationships betweenclimatic and hydrological changes in the upperMississippi riverbasin a SWAT and multi-GCM studyrdquo Journal of Hydrometeo-rology vol 11 no 2 pp 437ndash451 2010

[23] L Liu Z Liu X Ren T Fischer and Y Xu ldquoHydrologicalimpacts of climate change in the Yellow River Basin for the 21stcentury using hydrological model and statistical downscalingmodelrdquo Quaternary International vol 244 no 2 pp 211ndash2202011

[24] S Fall D Niyogi A Gluhovsky R A Pielke E Kalnay andG Rochon ldquoImpacts of land use land cover on temperaturetrends over the continental United States assessment using theNorth American Regional Reanalysisrdquo International Journal ofClimatology vol 30 no 13 pp 1980ndash1993 2010

[25] H Guo Q Hu and T Jiang ldquoAnnual and seasonal streamflowresponses to climate and land-cover changes in the Poyang Lakebasin Chinardquo Journal of Hydrology vol 355 no 1ndash4 pp 106ndash122 2008

[26] L Tang D Yang H Hu and B Gao ldquoDetecting the effect ofland-use change on streamflow sediment and nutrient losses bydistributed hydrological simulationrdquo Journal of Hydrology vol409 no 1-2 pp 172ndash182 2011

[27] M D Tomer and K E Schilling ldquoA simple approach todistinguish land-use and climate-change effects on watershedhydrologyrdquo Journal of Hydrology vol 376 no 1-2 pp 24ndash332009

[28] P F Juckem R J Hunt M P Anderson and D M RobertsonldquoEffects of climate and landmanagement change on streamflowin the driftless area ofWisconsinrdquo Journal of Hydrology vol 355no 1ndash4 pp 123ndash130 2008

[29] Q Feng G D Cheng and K N Endo ldquoTowards sustainabledevelopment of the environmentally degraded River Heihebasin Chinardquo Hydrological Sciences Journal vol 46 no 5 pp647ndash658 2001

[30] G-D Cheng H-L Xiao Z-M Xu J-X Li and M-F LuldquoWater issue and its countermeasure in the inland river basinsofNorthwest Chinamdasha case study inHeiheRiver Basinrdquo Journalof Glaciology and Geocryology vol 28 no 3 pp 406ndash413 2006

[31] A Jarvis H I Reuter A Nelson and E Guevara Hole-FilledSeamless SRTM Data V3 International Centre for TropicalAgriculture (CIAT) 2006

[32] X Deng Q Jiang H Su and F Wu ldquoTrace forest conversionsin Northeast China with a 1-km area percentage data modelrdquoJournal of Applied Remote Sensing vol 4 no 1 Article ID041893 pp 1ndash13 2010

[33] X Deng H Su and J Zhan ldquoIntegration of multiple datasources to simulate the dynamics of land systemsrdquo Sensors vol8 no 2 pp 620ndash634 2008

[34] X Deng J Liu Y Lin and C Shi ldquoA framework for the landuse change dynamics model compatible with rcmsrdquo Advancesin Meteorology vol 2013 Article ID 658941 7 pages 2013

[35] X Deng C Zhao and H Yan ldquoSystematic modeling ofimpacts of land use and land cover changes on regional climate

a reviewrdquo Advances in Meteorology vol 2013 Article ID 31767811 pages 2013

[36] X Deng F Yin Y Lin Q Jin and R Qu ldquoEquilibrium analyseson structural changes of land uses in Jiangxi Provincerdquo Journalof Food Agriculture and Environment vol 10 no 1 pp 846ndash8522012

[37] X Deng Q Jiang J Zhan S He and Y Lin ldquoSimulation on thedynamics of forest area changes in Northeast Chinardquo Journal ofGeographical Sciences vol 20 no 4 pp 495ndash509 2010

[38] X Deng C Zhao Y Lin et al ldquoDownscaling the impactsof large-scale LUCC on surface temperature along with IPCCRCPs a global perspectiverdquoEnergies vol 7 no 4 pp 2720ndash27392014

[39] S Neitsch J Arnold J E A Kiniry R Srinivasan and JWilliams ldquoSoil and water assessment tool userrsquos manual version2000rdquo GSWRL Report 02-06 2002

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EarthquakesJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MineralogyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geological ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geology Advances in

10 Advances in Meteorology

middle reaches of Heihe River Basin based on the simulationwith the SWAT model The results show that the water yieldwas more affected by climate change than by land use changeThis indicates that the predicted increase in precipitation willexert more significant impacts on the watershed hydrologythan the predicted land use changes will However the anal-ysis of the projected streamflow changes shows that there arehigher uncertainties in the dry season compared with thewet season in the simulation with the hydrological modeland GCMs climate data It is difficult to accurately projectthe hydrological changes since there are various uncertaintiesassociated with the future Green House Gas (GHG) emissionscenarios GCM structure downscaling method LULC andhydrological models In particular water resource managersare generally confronted with complex problems in sustain-able management and conservation of water resources dueto the uncertainties in the future hydrological projectionunder climate and land use changes It is therefore crucial toconsider both land use and climate changes in water resourceplanning for the Heihe River Basin so as to mitigate theirnegative hydrological impacts and more valuable informa-tion may be provided to the water resource managers if theseuncertainties in the future hydrological projection can beeffectively reduced through advancedmodeling and research

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was financially supported by themajor researchplan of the National Natural Science Foundation of China(Grant no 91325302) National Basic Research Program ofChina (973 Program) (no 2010CB950904) and the NationalNatural Science Funds of China for Distinguished YoungScholar (Grant no 71225005)

References

[1] Z ZaludM TrnkaMDubrovsky P Hlavinka D Semeradovaand E Kocmankova ldquoClimate change impacts on selectedaspects of the Czech agricultural productionrdquo Plant ProtectionScience vol 45 pp S11ndashS19 2009

[2] E Lorencova J Frelichova E Nelson and D Vackar ldquoPast andfuture impacts of land use and climate change on agriculturalecosystem services in the Czech Republicrdquo Land Use Policy vol33 pp 183ndash194 2013

[3] RMahmood A I Quintanar G Conner et al ldquoImpacts of landuseland cover change on climate and future research prioritiesrdquoBulletin of the AmericanMeteorological Society vol 91 no 1 pp37ndash46 2010

[4] Z Ma S Kang L Zhang L Tong and X Su ldquoAnalysis ofimpacts of climate variability and human activity on streamflowfor a river basin in arid region of northwest Chinardquo Journal ofHydrology vol 352 no 3-4 pp 239ndash249 2008

[5] B R Scanlon I Jolly M Sophocleous and L Zhang ldquoGlobalimpacts of conversions from natural to agricultural ecosystems

on water resources quantity versus qualityrdquo Water ResourcesResearch vol 43 no 3 Article ID W03437 2007

[6] L M Mango A M Melesse M E McClain D Gann and S GSetegn ldquoLand use and climate change impacts on the hydrologyof the upper Mara River Basin Kenya results of a modelingstudy to support better resource managementrdquo Hydrology andEarth System Sciences vol 15 no 7 pp 2245ndash2258 2011

[7] C J Vorosmarty P Green J Salisbury and R B LammersldquoGlobal water resources vulnerability from climate change andpopulation growthrdquo Science vol 289 no 5477 pp 284ndash2882000

[8] S Qi G Sun Y Wang S G McNulty and J A M MyersldquoStreamflow response to climate and landuse changes in acoastal watershed in North Carolinardquo Transactions of theASABE vol 52 no 3 pp 739ndash749 2009

[9] J Kim J Choi C Choi and S Park ldquoImpacts of changes inclimate and land useland cover under IPCC RCP scenarios onstreamflow in theHoeya River Basin Koreardquo Science of the TotalEnvironment vol 452-453 pp 181ndash195 2013

[10] Z Li W-Z Liu X-C Zhang and F-L Zheng ldquoImpacts ofland use change and climate variability on hydrology in anagricultural catchment on the Loess Plateau of Chinardquo Journalof Hydrology vol 377 no 1-2 pp 35ndash42 2009

[11] R W Skaggs D M Amatya G Chescheir C Blanton andJ Gilliam ldquoEffect of drainage and management practices onhydrology of pine plantationrdquo in Proceedings of the InternationalConference onHydrology andManagement of ForestedWetlands2006

[12] T D Prowse S Beltaos J T Gardner et al ldquoClimate changeflow regulation and land-use effects on the hydrology of thePeace-Athabasca-Slave system Findings from the NorthernRivers Ecosystem Initiativerdquo Environmental Monitoring andAssessment vol 113 no 1ndash3 pp 167ndash197 2006

[13] B Dixon and J Earls ldquoEffects of urbanization on streamflowusing SWAT with real and simulated meteorological datardquoApplied Geography vol 35 no 1-2 pp 174ndash190 2012

[14] E Lioubimtseva R Cole J M Adams and G KapustinldquoImpacts of climate and land-cover changes in arid lands ofCentral Asiardquo Journal of Arid Environments vol 62 no 2 pp285ndash308 2005

[15] Z Wang D L Ficklin Y Zhang and M Zhang ldquoImpact ofclimate change on streamflow in the arid Shiyang River Basinof northwest Chinardquo Hydrological Processes vol 26 no 18 pp2733ndash2744 2012

[16] D R DrsquoAgostino L G Trisorio N Lamaddalena and R RagabldquoAssessing the results of scenarios of climate and land usechanges on the hydrology of an Italian catchment modellingstudyrdquo Hydrological Processes vol 24 no 19 pp 2693ndash27042010

[17] V Mishra K A Cherkauer D Niyogi et al ldquoA regional scaleassessment of land useland cover and climatic changes onwater and energy cycle in the upper Midwest United StatesrdquoInternational Journal of Climatology vol 30 no 13 pp 2025ndash2044 2010

[18] J A Vano J A Foley C J Kucharik andM T Coe ldquoEvaluatingthe seasonal and interannual variations in water balance inNorthern Wisconsin using a land surface modelrdquo Journal ofGeophysical Research G Biogeosciences vol 111 no 2 2006

[19] D Mao and K A Cherkauer ldquoImpacts of land-use changeon hydrologic responses in the Great Lakes regionrdquo Journal ofHydrology vol 374 no 1-2 pp 71ndash82 2009

Advances in Meteorology 11

[20] J-Y Park M-J Park M-J H-K Joh et al ldquoAssessment ofMIROC32 hires climate and clue-s land use change impacts onwatershed hydrology using Swatrdquo Transactions of the ASABEvol 54 no 5 pp 1713ndash1724 2011

[21] S J Kim H J Kwon G A Park and M S Lee ldquoAssessmentof land-use impact on streamflow via a grid-based modellingapproach including paddy fieldsrdquoHydrological Processes vol 19no 19 pp 3801ndash3817 2005

[22] E Lu E S Takle and J Manoj ldquoThe relationships betweenclimatic and hydrological changes in the upperMississippi riverbasin a SWAT and multi-GCM studyrdquo Journal of Hydrometeo-rology vol 11 no 2 pp 437ndash451 2010

[23] L Liu Z Liu X Ren T Fischer and Y Xu ldquoHydrologicalimpacts of climate change in the Yellow River Basin for the 21stcentury using hydrological model and statistical downscalingmodelrdquo Quaternary International vol 244 no 2 pp 211ndash2202011

[24] S Fall D Niyogi A Gluhovsky R A Pielke E Kalnay andG Rochon ldquoImpacts of land use land cover on temperaturetrends over the continental United States assessment using theNorth American Regional Reanalysisrdquo International Journal ofClimatology vol 30 no 13 pp 1980ndash1993 2010

[25] H Guo Q Hu and T Jiang ldquoAnnual and seasonal streamflowresponses to climate and land-cover changes in the Poyang Lakebasin Chinardquo Journal of Hydrology vol 355 no 1ndash4 pp 106ndash122 2008

[26] L Tang D Yang H Hu and B Gao ldquoDetecting the effect ofland-use change on streamflow sediment and nutrient losses bydistributed hydrological simulationrdquo Journal of Hydrology vol409 no 1-2 pp 172ndash182 2011

[27] M D Tomer and K E Schilling ldquoA simple approach todistinguish land-use and climate-change effects on watershedhydrologyrdquo Journal of Hydrology vol 376 no 1-2 pp 24ndash332009

[28] P F Juckem R J Hunt M P Anderson and D M RobertsonldquoEffects of climate and landmanagement change on streamflowin the driftless area ofWisconsinrdquo Journal of Hydrology vol 355no 1ndash4 pp 123ndash130 2008

[29] Q Feng G D Cheng and K N Endo ldquoTowards sustainabledevelopment of the environmentally degraded River Heihebasin Chinardquo Hydrological Sciences Journal vol 46 no 5 pp647ndash658 2001

[30] G-D Cheng H-L Xiao Z-M Xu J-X Li and M-F LuldquoWater issue and its countermeasure in the inland river basinsofNorthwest Chinamdasha case study inHeiheRiver Basinrdquo Journalof Glaciology and Geocryology vol 28 no 3 pp 406ndash413 2006

[31] A Jarvis H I Reuter A Nelson and E Guevara Hole-FilledSeamless SRTM Data V3 International Centre for TropicalAgriculture (CIAT) 2006

[32] X Deng Q Jiang H Su and F Wu ldquoTrace forest conversionsin Northeast China with a 1-km area percentage data modelrdquoJournal of Applied Remote Sensing vol 4 no 1 Article ID041893 pp 1ndash13 2010

[33] X Deng H Su and J Zhan ldquoIntegration of multiple datasources to simulate the dynamics of land systemsrdquo Sensors vol8 no 2 pp 620ndash634 2008

[34] X Deng J Liu Y Lin and C Shi ldquoA framework for the landuse change dynamics model compatible with rcmsrdquo Advancesin Meteorology vol 2013 Article ID 658941 7 pages 2013

[35] X Deng C Zhao and H Yan ldquoSystematic modeling ofimpacts of land use and land cover changes on regional climate

a reviewrdquo Advances in Meteorology vol 2013 Article ID 31767811 pages 2013

[36] X Deng F Yin Y Lin Q Jin and R Qu ldquoEquilibrium analyseson structural changes of land uses in Jiangxi Provincerdquo Journalof Food Agriculture and Environment vol 10 no 1 pp 846ndash8522012

[37] X Deng Q Jiang J Zhan S He and Y Lin ldquoSimulation on thedynamics of forest area changes in Northeast Chinardquo Journal ofGeographical Sciences vol 20 no 4 pp 495ndash509 2010

[38] X Deng C Zhao Y Lin et al ldquoDownscaling the impactsof large-scale LUCC on surface temperature along with IPCCRCPs a global perspectiverdquoEnergies vol 7 no 4 pp 2720ndash27392014

[39] S Neitsch J Arnold J E A Kiniry R Srinivasan and JWilliams ldquoSoil and water assessment tool userrsquos manual version2000rdquo GSWRL Report 02-06 2002

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EarthquakesJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MineralogyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geological ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geology Advances in

Advances in Meteorology 11

[20] J-Y Park M-J Park M-J H-K Joh et al ldquoAssessment ofMIROC32 hires climate and clue-s land use change impacts onwatershed hydrology using Swatrdquo Transactions of the ASABEvol 54 no 5 pp 1713ndash1724 2011

[21] S J Kim H J Kwon G A Park and M S Lee ldquoAssessmentof land-use impact on streamflow via a grid-based modellingapproach including paddy fieldsrdquoHydrological Processes vol 19no 19 pp 3801ndash3817 2005

[22] E Lu E S Takle and J Manoj ldquoThe relationships betweenclimatic and hydrological changes in the upperMississippi riverbasin a SWAT and multi-GCM studyrdquo Journal of Hydrometeo-rology vol 11 no 2 pp 437ndash451 2010

[23] L Liu Z Liu X Ren T Fischer and Y Xu ldquoHydrologicalimpacts of climate change in the Yellow River Basin for the 21stcentury using hydrological model and statistical downscalingmodelrdquo Quaternary International vol 244 no 2 pp 211ndash2202011

[24] S Fall D Niyogi A Gluhovsky R A Pielke E Kalnay andG Rochon ldquoImpacts of land use land cover on temperaturetrends over the continental United States assessment using theNorth American Regional Reanalysisrdquo International Journal ofClimatology vol 30 no 13 pp 1980ndash1993 2010

[25] H Guo Q Hu and T Jiang ldquoAnnual and seasonal streamflowresponses to climate and land-cover changes in the Poyang Lakebasin Chinardquo Journal of Hydrology vol 355 no 1ndash4 pp 106ndash122 2008

[26] L Tang D Yang H Hu and B Gao ldquoDetecting the effect ofland-use change on streamflow sediment and nutrient losses bydistributed hydrological simulationrdquo Journal of Hydrology vol409 no 1-2 pp 172ndash182 2011

[27] M D Tomer and K E Schilling ldquoA simple approach todistinguish land-use and climate-change effects on watershedhydrologyrdquo Journal of Hydrology vol 376 no 1-2 pp 24ndash332009

[28] P F Juckem R J Hunt M P Anderson and D M RobertsonldquoEffects of climate and landmanagement change on streamflowin the driftless area ofWisconsinrdquo Journal of Hydrology vol 355no 1ndash4 pp 123ndash130 2008

[29] Q Feng G D Cheng and K N Endo ldquoTowards sustainabledevelopment of the environmentally degraded River Heihebasin Chinardquo Hydrological Sciences Journal vol 46 no 5 pp647ndash658 2001

[30] G-D Cheng H-L Xiao Z-M Xu J-X Li and M-F LuldquoWater issue and its countermeasure in the inland river basinsofNorthwest Chinamdasha case study inHeiheRiver Basinrdquo Journalof Glaciology and Geocryology vol 28 no 3 pp 406ndash413 2006

[31] A Jarvis H I Reuter A Nelson and E Guevara Hole-FilledSeamless SRTM Data V3 International Centre for TropicalAgriculture (CIAT) 2006

[32] X Deng Q Jiang H Su and F Wu ldquoTrace forest conversionsin Northeast China with a 1-km area percentage data modelrdquoJournal of Applied Remote Sensing vol 4 no 1 Article ID041893 pp 1ndash13 2010

[33] X Deng H Su and J Zhan ldquoIntegration of multiple datasources to simulate the dynamics of land systemsrdquo Sensors vol8 no 2 pp 620ndash634 2008

[34] X Deng J Liu Y Lin and C Shi ldquoA framework for the landuse change dynamics model compatible with rcmsrdquo Advancesin Meteorology vol 2013 Article ID 658941 7 pages 2013

[35] X Deng C Zhao and H Yan ldquoSystematic modeling ofimpacts of land use and land cover changes on regional climate

a reviewrdquo Advances in Meteorology vol 2013 Article ID 31767811 pages 2013

[36] X Deng F Yin Y Lin Q Jin and R Qu ldquoEquilibrium analyseson structural changes of land uses in Jiangxi Provincerdquo Journalof Food Agriculture and Environment vol 10 no 1 pp 846ndash8522012

[37] X Deng Q Jiang J Zhan S He and Y Lin ldquoSimulation on thedynamics of forest area changes in Northeast Chinardquo Journal ofGeographical Sciences vol 20 no 4 pp 495ndash509 2010

[38] X Deng C Zhao Y Lin et al ldquoDownscaling the impactsof large-scale LUCC on surface temperature along with IPCCRCPs a global perspectiverdquoEnergies vol 7 no 4 pp 2720ndash27392014

[39] S Neitsch J Arnold J E A Kiniry R Srinivasan and JWilliams ldquoSoil and water assessment tool userrsquos manual version2000rdquo GSWRL Report 02-06 2002

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EarthquakesJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MineralogyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geological ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geology Advances in

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ClimatologyJournal of

EcologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EarthquakesJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom

Applied ampEnvironmentalSoil Science

Volume 2014

Mining

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal of

Geophysics

OceanographyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

GeochemistryHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Atmospheric SciencesInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MineralogyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MeteorologyAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Paleontology JournalHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geological ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Geology Advances in