modelling hydrological response to different land-use and climate change scenarios in the zamu river...

HYDROLOGICAL PROCESSESHydrol. Process. 22, 2502–2510 (2008)Published online 22 October 2007 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/hyp.6846

Modelling hydrological response to different land-use andclimate change scenarios in the Zamu River basin of

northwest China

Sufen Wang,1 Shaozhong Kang,1* Lu Zhang2 and Fusheng Li3

1 Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, People’s Republic of China2 CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia

3 Agricultural College, Guangxi University, Nanning, Guangxi 530005, People’s Republic of China

Abstract:

Changes in climate and land use can significantly influence the hydrological cycle and hence affect water resources.Understanding the impacts of climate and land-use changes on streamflow can facilitate development of sustainable waterresources strategies. This study investigates the flow variation of the Zamu River, an inland river in the arid area of northwestChina, using the Soil and Water Assessment Tool distributed hydrological model. Three different land-use and climate-changescenarios were considered on the basis of measured climate data and land-use cover, and then these data were input into thehydrological model. Based on the sensitivity analysis, model calibration and verification, the hydrological response to differentland-use and climate-change scenarios was simulated. The results indicate that the runoff varied with different land-use type,and the runoff of the mountain reaches of the catchment increased when grassland area increased and forestland decreased.The simulated runoff increased with increased precipitation, but the mean temperature increase decreased the runoff under thesame precipitation condition. Application of grey correlation analysis showed that precipitation and temperature play a criticalrole in the runoff of the Zamu River basin. Sensitivity analysis of runoff to precipitation and temperature by considering the1990s land use and climate conditions was also undertaken. Copyright 2007 John Wiley & Sons, Ltd.

KEY WORDS changing climate; land-use scenario; runoff; SWAT

Received 7 February 2007; Accepted 12 June 2007

INTRODUCTION

The inland rivers of the arid region in northwest Chinaare surrounded by alps and highlands, which formthe distinctive hydrological systems of this arid area.Generally, runoff is generated from the upper moun-tain reaches of the river basin and disappears in theplain. Thus, runoff from the upper reach directly affectsthe ecosystem and human activities in the oasis downreach, and the ecosystem in this region is quite frag-ile, Because of the influence of global climate changeand human activities in this region, the ecosystemof part of the area has been degraded; it has beendifficult to restore the degraded ecosystem and thishas resulted in a series of severe ecological prob-lems, such as decreased forest areas, degradation ofgrasslands, and severe effects of wind and sand. Dry-ing up of reservoirs and lakes and the decline ofgroundwater tables are other examples of environmen-tal problems in the arid region of northwest China. TheZamu River is one of the tributaries of the ShiyangRiver basin, accounting for 16Ð4% of the total basinrunoff. Therefore, study of the runoff variation of theZamu River and the hydrological response to climate

* Correspondence to: Shaozhong Kang, Center for Agricultural WaterResearch in China, China Agricultural University, Beijing 100083,People’s Republic of China. E-mail: [email protected]

change and land-use changes are important for devel-oping management strategies for ecological preservationand sustainable utilization of water resources in thisregion.

The influence of climate and land-use change oncatchment water balance is a priority in hydrologicalstudy. Climate change affects the amount and distri-bution of regional precipitation and temperature, andthence affects catchment runoff. There are two meth-ods to study the effect on the hydrological cycle. Thefirst method is based on meteorological data. Differentclimate-change scenarios were set up based on historicalmeteorological data and then used in hydrological mod-els for hydrological simulation. And different methodscan be used for the construction of regional climate-change scenarios (Viner et al., 1995). The second methodis the combination of a climate model and a hydrolog-ical model, the climate change being simulated usinga climate model, such as a general circulation model(GCM) or a regional circulation model (RCM), and thenthe outputs of the climate model are used in a hydro-logical model for hydrological simulation (Arnell, 1992,2003; Arnell and Reynard, 1996). Booij (2005) reporteda study in which a climate model and a hydrologicalmodel were combined to study hydrological responseto climate change. Studies on the hydrological response

Copyright 2007 John Wiley & Sons, Ltd.

MODELLING HYDROLOGICAL RESPONSE TO CHANGE IN LAND USE AND CLIMATE 2503

to different climate-change scenarios have mainly con-centrated on runoff (Sefton and Boorman, 1997; Guoet al., 2002; Chen et al., 2005) and flooding (Booij,2005).

Land-use change influences land-cover type, alters sur-face runoff generation, and then affects the catchmenthydrological process. Studies on the effect of land-usechange on the water cycle concentrate mainly on meanannual runoff (Brown et al., 2005). Methods used includepaired catchment studies and hydrological modelling.Paired catchment studies can provide direct evidence ofland-use change impacts on runoff; however, they gener-ally require long durations and cover small study areas.Thus, hydrological models are becoming an importantmethod to study the effects of land-use change on thehydrological cycle (Karvonen et al., 1999; Felix et al.,2002), especially the distributed hydrological modelsbased on a physical foundation. Because of the distributednature, the models can simulate the spatial distribution ofchanges in key water balance components in the basin;thus, a hydrological model becomes a major tool to studythe hydrological cycle. Distributed hydrological modelscan also be employed as effective tools to predict theconsequences of land-use change (Bhaduri et al., 1997;Fohrer et al., 2001, 2005; Krysanova et al., 2005).

In recent years, the Soil and Water Assessment Tool(SWAT) model has been widely used in many countries.It has several advantages, such as multiple functions, amodular design and only a few parameters need to beoptimized compared with many other hydrological mod-els. The SWAT model is primarily used in precipitationrunoff analysis (Arnold et al., 1993; Arnold and Allen,1996), climate change effect on water cycle (Stonefeltet al., 2000; Muttiah and Wurbs, 2002; Eckhardt andUlbrich, 2003) and land-use change effect on the watercycle (Fohrer et al., 2001; Tripathi et al., 2005). TheSWAT model can be employed as an effective tool tosimulate precipitation–runoff relationships under land-use change conditions (Hernandez et al., 2000). Huismanet al. (2004) used the eco-hydrological model SWAT-G tostudy the sensitivity of the model simulations to changesin soil properties during land-use change of the Dill catch-ments in Germany. The SWAT model can also be linkedwith other models to study the effects of land-use pat-terns on hydrological landscape functions (Fohrer et al.,2005).

Since the SWAT model was developed, it has not onlybeen used in the USA, but has also been used successfullyin other countries. However, there are few reports onthe SWAT model being used for mountain catchmentsin arid regions. Until now, SWAT has not been used insimulating the influence of climate and land-use changeon hydrological processes in the inland river basins ofthe arid region in northwest China. The objectives of thisstudy are: (1) to validate the SWAT model in terms ofrunoff for a mountainous catchment in the arid region ofnorthwest China; (2) to simulate the impacts of differentclimate scenarios on runoff; and (3) to simulate howdifferent land-use scenarios affect runoff.

MATERIALS AND METHODS

Experimental watershed

The Zamu River originates in the Qilian Mountainsand has a catchment area of 851 km2. It is the onlyunregulated river in the Shiyang River basin in thearid region of northwest China and has a glacier areaof 3Ð74 km2 in the mountain area in the upper reach(Figure 1). The only gauging station in the catchmentis the Zamusi hydrological station. The elevation ofthe catchment varies from 2000 to 4802 m above sealevel and its catchment shape is plumose. The meanflow of the river (1955–2003) measured at the Zamusihydrological station is 7Ð70 m3 s�1. The upper ZamuRiver catchment has good vegetation cover, with alpinemeadow, alpine grassland, shrubs and arbours. Forestlandis patchy, with a mixed distribution of grassland andforestland. The current land-use map of the Zamu Riverbasin is shown in Figure 2. The major land-use typescan be divided into grassland, forestland, farmland, ruralresidential land and bare land; the proportions of thedifferent land use types in the catchment are shown inTable I. The digital elevation model (DEM) in the ZamuRiver catchment has a resolution of 100 m ð 100 m(Figure 3). There are three types of soil in the catchment:sierozem, irrigated desert soil and mountain meadow soil.The mountain meadow soil occupies about 99Ð39% of thetotal catchment area.

Figure 1. Location of the Zamu River basin

Grassland (intens. > 50%)

FarmlandForest

Grassland (intens. 20% ∼ 50%)Grassland (intens. 5% ∼ 20%)RuralOthersBare land

Figure 2. Land-use map of the Zamu River basin

Copyright 2007 John Wiley & Sons, Ltd. Hydrol. Process. 22, 2502–2510 (2008)DOI: 10.1002/hyp

2504 S. WANG ET AL.

Figure 3. DEM of the Zamu River basin

Table I. Proportion of different land-use type in the Zamu Riverbasin

Land usetype

Farm-land

Forest-land

Grass-land

Ruralresidential

land

Others Bareland

Area (%) 1Ð12 33Ð86 47Ð64 0Ð02 0Ð01 17Ð35

Model

The study simulated runoff yield using the SWATmodel (Arnold et al., 1998). SWAT is a distributedprocess-based hydrological model that can operate underdifferent climate and land-use change scenarios. Themodel can simulate long-term hydrological change usinga daily or monthly time step. Using spatial informationprovided by a geographical information system (GIS)and remote sensing (RS) system, the model can simulatemultiple hydrological physical processes involving waterquantity and quality, including the transport and trans-forming processes of water, sand, chemical substancesand insecticide in complicated large river basins. Hydro-logical processes simulated by the SWAT model includerunoff generation and flow routing. The model can repre-sent the effect of climate factors, such as precipitation andevaporation, and the spatial change of land surface factorson the hydrological cycle in the river basin. The impactsof climate change can be simulated with the SWAT modelby manipulating the climatic input. The water balanceequation in the SWAT model is expressed as (Arnoldet al., 1998)

SWt D SW0 Ct∑

iD1

�Rday � Qsurf � Ea � wseep � Qgw�

�1�where SWt (mm) is the final soil water content, SW0

(mm) is the initial soil water content, t (days) is the time,Rday (mm) is the amount of precipitation on day i, Qsurf

(mm) is the amount of surface runoff on day i, Ea (mm)is the amount of evapotranspiration on day i, wseep (mm)is the amount of percolation that bypasses the soil profilebottom on day i, and Qgw (mm) is the amount of returnflow on day i.

The model provides two methods of surface runoffcalculation: one is the runoff curve number methoddeveloped by the Soil Conservation Service (SCS) ofthe United States Department of Agriculture (USDA,1986) and the other is through the Green–Ampt infil-tration method (Green and Ampt, 1911). In most cases,the runoff curve number method is better than theGreen–Ampt method (Loague and Freeze, 1985); thus,the runoff curve number method was used in our study.

Potential evapotranspiration is calculated by thePenman–Monteith equation (Monteith, 1965). Soil evap-oration is a function of soil depth, soil moisture andpotential evapotranspiration; vegetation transpiration isdependent on vegetation growth status, and it is a func-tion of potential evapotranspiration, soil root depth andleaf area index.

Soil interflow is calculated by the kinematical storagemodel (Sloan and Moore, 1984). The model takes soilhydrological conductance, topographical slope and thetemporal and spatial change of soil moisture into account.The baseflow is calculated using the following equation(Arnold et al., 1998):

Qgw,i D Qgw,i�1 exp��˛gwt�

C wrchrg[1 � exp��˛gwt�] �2�

where Qgw,i (mm) is the groundwater flow into the mainchannel on day i, Qgw,i�1 (mm) is the groundwater flowinto the main channel on day i � 1, ˛gw is the baseflowrecession constant, t is the time step (1 day), and wrchrg

(mm) is the amount of recharge entering the aquifer onday i.

Input data

Before the SWAT model begins computation, the DEMof the river basin should be input into the model. Forthe Zamu River basin, the topographical data to formthe DEM was obtained from 1 : 250 000 resolution maps.For simulating runoff, the following databases need to beestablished: meteorological data, soil properties informa-tion and land-use type data. The meteorological databaseincludes the spatial distribution of meteorological sta-tions, daily precipitation, maximum and minimum tem-peratures, wind speed and relative humidity. The meteo-rological database in the Zamu River basin is composedof daily precipitation, maximum and minimum temper-atures, wind speed and relative humidity from 1951 to2001. Vegetation data were obtained from a land-use map.The soil properties database includes soil particle compo-sition, saturated hydraulic conductivity and bulk density.For the SWAT database, the soil properties are basedon US information, so a local soil properties databaseneeds to be established. A soil properties database forthe Zamu River basin was established and based on theChina soil database and a soil correlation system com-bined with soil water characteristics software: SPAW(soil–plant–air–water) developed by Washington StateUniversity, USA (http://hydrolab.arsusda.gov/soilwater).



The China soil correlation system determined the ref-erence criterion between a generic soil classification inChina and US soil taxonomy (http://www.issas.ac.cn).The main soils properties in the Zamu River basin areshown in Table II.

Subdivision of catchment

Based on the terrain characteristics of the catchment,the area is divided into a number of subcatchments whichdepend not only on the catchment area, but also on thesimulation accuracy. Using a 100 m ð 100 m DEM forthe Zamu River basin, the catchment was divided into7, 11, 25, 32 and 42 subdivisions, and then runoff wassimulated for the subdivision set. The correlation coeffi-cients between the simulated and measured total runoffwere 0Ð75, 0Ð81, 0Ð78, 0Ð76, and 0Ð76 for the five differ-ent subdivisions. The correlation coefficient was highestwhen the catchment was divided into 11 subcatchments.When studying the effect of catchment subdivision onmodel performance, Romanowicz et al. (2005) showedthat a non-linear relationship exists between model per-formance and the number of subcatchments and that anincreased number of subdivisions can result in decreasedmodel accuracy. The area of 11 subdivisions ranged from39Ð73 km2 to 113Ð4 km2, average slope of tributary chan-nel ranged from 0Ð048 to 0Ð136, and the longest tribu-tary channel length ranged from 5Ð395 km to 41Ð732 km.Figure 4 shows the subcatchments of the Zamu Riverbasin.

Sensitivity analysis

Sensitivity analysis is important for distributed hydro-logical models with many parameters. The LH-OATanalysis method (Van Griensven et al., 2006) was used

Figure 4. Sub-basin of the Zamu River basin

in this study. Only streamflow was used in the sensi-tivity analysis based on daily observations and modelsimulations for the period of 1995–1996. The rangesof parameters used in sensitivity analysis are shown inTable III.

Model calibration and verification

Model calibration is an important component of hydro-logical modelling. In this study, monthly values of pre-cipitation, potential evapotranspiration, and streamflowwere used in the model calibration. The dataset wasdivided into two parts: a calibration period (1983–1990)and a verification period (1991–2001). A ‘global search’method (shuffled complex evolution; Duan et al., 1992)was applied for parameter optimization. The objectivefunction was defined based on Nash–Sutcliffe efficiency

Table II. Soil characteristics in the Zamu River basin

Item Mountain meadow soil Irrigated desert soil Sierozem

Soil depth (cm) 20 60 100 28 100 150 21 45 107 150Bulk density (g cm�3) 1Ð18 1Ð18 1Ð18 1Ð3 1Ð3 1Ð3 1Ð2 1Ð2 1Ð4 1Ð4Available water capacity (mm mm�1) 0Ð11 0Ð11 0Ð11 0Ð14 0Ð13 0Ð13 0Ð14 0Ð14 0Ð14 0Ð22Saturated conductivity (mm h�1) 1Ð05 1Ð15 1Ð15 2Ð57 2Ð08 2Ð08 2Ð18 3Ð73 3Ð57 3Ð26Clay content (%) 59 47 56 44 19 16 21 14 16 12Silt content (%) 37 49 30 23 38 41 36 43 51 75Sand content (%) 4 4 14 33 43 43 43 43 33 33

Table III. Sensitivity results for the SWAT parameters for streamflow in the Zamu River basin

Parameter Lower bound Upper bound Rank Definition Process

CN2a �50 50 1 Initial SCS CN II value RunoffSOL AWCa �50 50 2 Available water capacity SoilCANMXb 0 10 3 Maximum canopy storage RunoffESCOb 0 1 4 Soil evaporation compensation factor EvaporationSFTMPb 0 5 5 Snow fall temperature SnowALPHA BFb 0 1 6 Baseflow recession constant GroundwaterSOL Ka �50 50 7 Saturated hydraulic conductivity (mm h�1) Soil

a These parameters were varied according to a relative changes from �50% to C50%.b These parameters were changed with actual values between lower bound and upper bound.


2506 S. WANG ET AL.

index:

NSE D 1 �

iDn∑

iD1

�qobsm � qsimm�2

iDn∑

iD1

�qobsm � qobs�2

�3�

where qobsm is the measured monthly streamflow, qsimm

is the simulated monthly streamflow, qobs is the mean ofthe measured monthly streamflow, and n is the numberof measurement.

The model parameters to be optimized were initial SCSCN II value, soil evaporation compensation factor andbaseflow factor. Selection of these parameters was basedon the sensitivity analysis and control that these factorsexerted on streamflow. The remaining model parameterswere either estimated from the local meteorological dataand soil properties or set to default values provided bythe model developers.

Grey correlation analysis

Grey correlation analysis is a factor comparison anal-ysis method that can identify the major factors affectingthe objective value and analyse the correlation betweendifferent factors (Deng, 1996):

�i�k� Dmin

imin

ki�k� C �max

imax

k

i�k�

i�k� C �maxi

maxk

i�k��4�

where �i�k� is the correlation coefficient, i�k� is theabsolute difference of different paired values between thecomparison series and the reference series, � �0 < � < 1�is the differentiate coefficient which influences �i�k�.The lower � is, the better the differentiate effect is; ingeneral � D 0Ð5. �i is the grey correlation degree, whichis calculated using the weighted average method as

�i D 1

n

n∑

kD1

�i�k� �5�

As the comparison and reference series become closer,�i is greater, which shows that change trend of com-parison series and the reference series approaches unity.The correlation is significant when �i is greater than 0Ð5,markedly significant when greater than 0Ð7.

RESULTS AND DISCUSSION

Sensitivity analysis

The results of sensitivity analysis are listed in Table IIIbased on their ranking. A parameter ranked as 1 isconsidered the most important and ranks 2–7 are lessimportant. From Table III, we can see that that SCSrunoff curve number for moisture condition II (CN2)shows high sensitivity for simulated streamflow. Thesoil water capacity ‘SOL AWC’ and saturated hydraulicconductivity ‘SOL K’ are also important. The resultsshow that parameters representing the soil properties,

surface runoff, groundwater, and snow process are sen-sitive. Therefore, accurate estimation of these param-eters is important for streamflow simulation with theSWAT model in the inland river basin of the arid areain northwest China. The results of the sensitivity analy-sis for parameters CN2 and baseflow recession constant(ALPHA BF) are consistent with those of Van Griensvenet al. (2006).

Calibration and verification analysis

Before the model was calibrated, the monthly runoffduring the period of 1983–1990 was simulated by theSWAT model as shown in Figure 5. The Nash–Sutcliffeefficiency (NSE) for the simulated runoff is 0Ð79 andthe correlation coefficient is 0Ð82. However, it canbe seen that the winter-month flows were consistentlyunderestimated by the model and peak flows were alsounderestimated for some years. These results indicatethat the winter-month streamflow is mainly sustained bybaseflow in the Zamu River catchment and that the modelfailed to simulate the baseflow component accurately.The SWAT model calculates baseflow from groundwaterrecharge and a recession constant (Smedema and Rycroft,1983). The baseflow recession constant (ALPHA BF)controls the rate of baseflow and is a sensitive parameterlisted for the Zamu River catchment, as shown in thesensitivity analysis.

Glacier and snow are present in the Zamu River catch-ment and on melting contribute to streamflow during theperiod March–October After calibration, the linear cor-relation coefficient between the measured and simulatedvalues increased to 0Ð89 and NSE increased to 0Ð85,indicating improved model performance. Table IV liststhe optimized model parameter values together with thelocally estimated parameters. For the verification dataset,the correlation coefficient is 0Ð85 and NSE is 0Ð82, show-ing results comparable to the calibration dataset. Thus,the SWAT model can be used to simulate the flow of theZamu River. The results after calibration and verificationare shown in Figure 6.

Scenarios under different land uses

The main land-use types in the Zamu River basin aregrassland and forestland, occupying 80Ð5% of the total

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Figure 5. Comparison of the simulated and monthly measured streamflowin the Zamu River basin before calibration



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Figure 6. Comparison of the simulated and monthly measured streamflow in the Zamu River basin after calibration

Table IV. Optimal model parameters for the runoff simulation inthe Zamu River basin

List Parameter Value

1 Precipitation lapse rate (dp/dz) 0Ð72 Temperature lapse rate (dT/dz) 4Ð53 Maximum melt factor (mm °C�1) 5Ð504 Minimum melt factor (mm °C�1) 4Ð005 100% snow cover threshold (mm) 3006 50% snow cover threshold 0Ð507 Soil evaporation compensation factor 0Ð908 Plant uptake compensation factor 0Ð209 Baseflow recession constant 0Ð005

catchment area (see Table I). To understand the impactsof land-use changes on streamflow in the catchments,three scenarios were considered. Scenario 1 assumesthat all the current grassland will be converted intoforestland, and the remaining land uses remain constant.This represents a 140% increase in forest area in thecatchment. Scenario 2 assumes that 25% of the currentgrassland will be changed into forestland. Scenario 3assumes that all the current forestland will be convertedinto grassland for grazing, representing a 71% increase ingrassland area. These land-use scenarios are based on thecurrent land-use conditions and possible future land-useoptions in the catchments. The percentage areas of thethree scenarios are shown in Figure 7.

Responses of streamflow to different land-use scenarios

The calibrated SWAT model was used to simulate themonthly flow of Zamu River under the three land-usescenarios for the period from January 1992 to December2001. The results for the three land-use scenarios areshown in Figure 8 and Table V and indicate that the meanannual streamflow is reduced by 6Ð9 mm (or 2Ð3%) underthe scenario that all current grassland area is convertedto forestland, and 0Ð2 mm (or 0Ð01%) if only 25% of thecurrent grassland area is converted into forestland. This

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Land

use

pro

port

ions

(%

) farmland forest grasslandothers rural bare land

Scenario 2 Scenario 3

Figure 7. Land-use proportion of different scenarios in the Zamu Riverbasin

is because trees generally use more water and, hence, lessrunoff will be generated from forested catchments (Zhanget al., 2001). The results also show that the mean annualstreamflow increases with decreased forest area, and a33Ð9% reduction in forest area leads to a 3Ð4% increasein streamflow. Weber et al. (2001) showed that simulatedstreamflow by the hydrological model increased with areduction of forestland area and an increase of grasslandarea.

Effects of climate change on streamflow

In order to study the response of streamflow to histori-cal climate change, three simulations were conducted by

Table V. Changes in average annual runoff under differentland-use scenarios

Item Scenario1

Scenario2

Scenario3

Actuality

Runoff (mm) 287Ð9 294Ð6 302Ð0 294Ð8Absolute changea

(mm)�6Ð9 �0Ð2 C10Ð0 0

Percentage change (%) �2Ð3 �0Ð01 C3Ð4 0

a Compared with the simulated results of actuality.


2508 S. WANG ET AL.

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eam

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scenario 2

scenario 3

Figure 8. Simulation of monthly streamflow of three land-use scenarios in the Zamu River basin

assuming constant land-use conditions of the 1990s andvarying the climates of different decades (see Table VI).Compared with the 1990s, the average precipitation inthe 1960s was lower, representing a dry period, whereasthe 1980s was a wetter period with over 25% more pre-cipitation.

Table VII shows that the simulated flow is highestduring the 1980s and lowest using the 1960s climate.During March to May every year, because streamflow inthe Zamu River basin mainly comes from the snowmelt,the air temperature in these three months will affectthe amount of snowmelt. Compared with the climateof the 1990s, the simulated mean annual streamflowdecreased by 11Ð6 mm (or 4Ð74%) for a 7Ð6% reduction inmean annual precipitation and a 0Ð64 °C reduction in airtemperature. However, a 7Ð46 mm (or 1Ð9%) reduction inprecipitation and a 0Ð77 °C in air temperature result in a2Ð1 mm (or 0Ð8%) increase in mean annual streamflow.These results indicate that the distribution of precipitationand air temperature is important for runoff generation.Finally, a 26Ð55 mm (or 6Ð8%) increase in mean annual

Table VI. Average runoff, precipitation and air temperature indifferent decade in the Zamu River basin

Item 1960s 1970s 1980s 1990s

Precipitation (mm) 361Ð34 398Ð39 417Ð48 390Ð93Mean annual

temperature (°C)�0Ð22 �0Ð36 �0Ð28 0Ð42

Mean temperature ofMarch to May (°C)

�0Ð22 �0Ð52 �0Ð49 0Ð15

Runoff amount(m3 s�1)

6Ð98 7Ð43 7Ð68 6Ð64

Runoff depth (mm) 258Ð55 275Ð15 284Ð60 244Ð10Runoff coefficient 0Ð72 0Ð69 0Ð68 0Ð62Change of

precipitationa (mm)�29Ð59 7Ð46 26Ð55 0Ð00

Changing percentage(%)

�7Ð6 C1Ð9 C6Ð8 0Ð00

Change of meanannual temperaturea

(°C)

�0Ð64 �0Ð78 �0Ð69 0Ð00

a Compared with the results of 1990s.

precipitation and a 0Ð69 °C decrease in air temperatureled to 11Ð9 mm (or 4Ð87%) increase in mean annualstreamflow (see Table VII).

In order to understand how precipitation and air tem-perature affect the streamflow, annual values of stream-flow, precipitation, and air temperature during the period1960–1991 were correlated using grey correlation anal-ysis. From Equations (4) and (5), the grey correlationdegree between the streamflow and air temperature is0Ð73, and the grey correlation degree between the stream-flow and precipitation is 0Ð76; both are greater than 0Ð70,indicating that streamflow was closely related to bothprecipitation and air temperature. Therefore, the runoffin the mountainous area of the arid region of northwestChina is mainly affected by precipitation and air tem-perature. The effect of precipitation on flow is greaterthan that of air temperature because the correlation coef-ficient between the flow and precipitation is greater thanthat between the flow and air temperature, indicating thatprecipitation is the determining factor of flow formationin the Zamu River basin. But precipitation and air tem-perature affect the flow differently in different seasons ina year. Figure 9 shows the correlation coefficient betweenmonthly flow and mean temperature and the correlationcoefficient between monthly flow and precipitation byanalysing the relationship between the measured flow andmean temperature or precipitation during 1960–2001.

Table VII. Simulated runoff in the Zamu River basin for differentcombined scenarios

Item Combinedscenario

1

Combinedscenario

2

Combinedscenario

3

1990s

Simulated (mm) 233Ð5 247Ð3 257Ð1 245Ð1Changing amounta

(mm)�11Ð6 C2Ð1 C11Ð9 0Ð0

Changingpercentage (%)

�4Ð74 C0Ð8 C4Ð87 0Ð0

a Compared with the simulated results of 1990s.



The results show that the streamflow was better corre-lated with precipitation than temperature in the summerseason; however, the correlation coefficient between flowand temperature was greater in the spring snowmelt sea-son, indicating the effect of air temperature on snowmeltrunoff. Rainfall plus glacier melt and snowmelt are themain components of river flow in the summer season.After March, air temperature increases, glacier and snowcommence to melt, and the river flow increases month bymonth. During May to September, rainfall accounts for80% of total annual precipitation, streamflow in the sameperiod also accounts for 80Ð4% of total annual stream-flow, and mean maximum monthly flow is 23 times themean minimum monthly flow. Thus, rainfall is an impor-tant factor affecting the runoff of this mountainous river.

Figure 10 shows that the flow in both the summer andautumn seasons accounts for 70% of total flow in theZamu River, and precipitation and flow change consis-tently in different years. This is because air temperatureaffects the flow in two ways: increased air temperaturewill affect snowmelt, leading to more runoff; the othereffect is on potential evaporation, i.e. potential evapora-tion increases when temperature increases, which leads toa reduction of flow at the outlet of the mountain catch-ment. Under the same precipitation condition, how doesthe flow respond to the air temperature? The runoff for theperiod 1990–1999 was simulated using the SWAT modelby increasing the mean annual air temperature by 1, 2,3 and 4 °C. The results are given in Table VIII and indi-cate that the annual mean runoff in the Zamu River basin

0.0

0.1

0.2

0.3

0.4

0.5

0.6

1 2 3 4 5 6 7 8 9 10 11 12

Month

Cor

rela

tion

coef

ficie

nt

PrecipitationTemperature

Figure 9. Correlation coefficient between monthly measured runoff ofZamu River and temperature and precipitation during 1960–2001

decreases with increased temperature under the same pre-cipitation condition. When temperature is increased by1 °C, the flow is reduced by about 1Ð1%.

Under the same temperature condition, how doesthe flow respond to the preciptation? The runoff forthe period 1990–1999 was simulated using the SWATmodel by increasing the precipitation by 3, 5, 10 and15%. The results in Table VIII indicate that the annualmean streamflow in the Zamu River catchment increaseswith increased precipitation under the same temperaturecondition. When precipitation is increased by 3%, theflow increases by 10 mm. The results in Table VIIIalso show that changes in precipitation result in greaterpercentage changes in streamflow, which is consistentwith the findings of other reported studies (Chiew et al.,1995; Chiew and McMahon, 2002).

CONCLUSIONS

The SWAT model was applied to the arid region ofnorthwest China to examine the effect of snow, rainfalland temperature changes and land-use change on thehydrological cycle in the area. Sensitivity analysis, modelcalibration and verification were carried out to determinethe most appropriate parameters in the SWAT model,which was used to simulate the hydrological responseto land-use change and climate change.

In the spring season (March–May), air temperatureaffects runoff through snowmelt. The results indicatethat the flows increase in the spring season with theincrease in temperature. Under the same precipitationcondition, the annual flow was reduced by about 1Ð1%when temperature is increased by 1 °C. Under the sametemperature condition, a 10% increase in precipitationresulted in a 13% increase in mean annual streamflow.

Table VIII. Sensitivity analysis of runoff to precipitation andtemperature by considering the 1990s land-use and climate

conditions

Temperature change (°C) C1 C2 C3 C4Runoff change (mm) �1Ð13 �2Ð26 �3Ð01 �4Ð52Precipitation change (%) C3 C5 C10 C15Runoff change (mm) C10 C18 C38 C59

0

10

20

30

40

50

60

1960s 1970s 1980s 1990s

Run

off p

ropo

rtio

ns (

%)

Runoff in spring Runoff in summerRunoff in autumn Runoff in winter

Figure 10. Mean 10-year runoff proportion in different seasons in the Zamu River basin


2510 S. WANG ET AL.

The simulated streamflow increases with an increasedgrassland area and a reduced forestland area. Annualstreamflow is reduced by less than 1% when the currentgrassland area is reduced below 25%.

ACKNOWLEDGEMENTS

We are grateful to the research grants from the ChineseNational Natural Science Fund (50339030, 50528909).

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