Relative effects of human activities and climate change on theriver runoff in an arid basin in northwest China
Wen Dong,1 Baoshan Cui,1* Zhihui Liu2 and Kejiang Zhang31 School of Environment, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Normal University, Beijing
100875, China2 Arid Ecological Environment Institute, Xinjiang University, Xinjiang 830046, China
3 Xinjiang Deland Co., Ltd., Xinjiang 830000, China
KEY WORDS runoff variation; human activities; climate change; SWAT; Jinghe River Basin
Received 4 March 2013; Accepted 16 July 2013
INTRODUCTION
The increase in water demand for production and dailylife has motivated humans to construct numerouswater control systems, including dams and reservoirs(Moore et al., 2012; Tebakari et al., 2012). Despitetheir value for water mediation, water control systemscan negatively affect ecosystems (Wei et al., 2007;Costa and Soares, 2012). For example, building damsand reservoirs has dramatically decreased annualrunoff over the past decades (Liu et al., 2005; Poffet al., 2007). The consequences of water controlsystems on ecosystems are likely to be severe in aridand semi-arid regions, where a decrease in river runoffdownstream can aggravate desertification, soil erosion,soil salinization and so on. The ecological conse-quences of dams and reservoirs in these regions have
orrespondence to: Baoshan Cui, School of Environment, State Keynt Laboratory of Environmental Simulation and Pollution Control,ijing Normal University, Beijing 100875, China.ail: [email protected]
been well documented in previous studies (e.g. Canzianiet al., 2006; Richter and Thomas, 2007).Aside from human activities, climatic variation, such
as extreme drought and climatic warming, can alsoaffect river runoff (Bae et al., 2008; Stewart, 2009).The change in climatic variables (temperature andprecipitation) is also reflected by runoff variation.Rising temperature can result in higher evaporation,ice-melting and the alteration of precipitation patterns,which directly or indirectly affect changes in riverrunoff. This effect has been previously examined usingclimate models (Chiew et al., 2009; Zhang et al., 2011),large-scale hydrological models with global climatemodel derived scenarios of future climate change (Tothet al., 2006; Nurmohamed et al., 2007) and rainfall-runoff models (Wu and Johnston, 2007; Rose, 2009).These models simulate the effects of historical climaticvariables (temperature and precipitation) on river runoffand predict the potential impact of future climaticvariation on river runoff. However, the reliability anduncertainty of model outputs require further improve-ment through the modification of the existing modelstructure or model parameter inputs.
Figure 1. Location of the study area
W. DONG ET AL.
Recent research has begun to examine the relative rolesof human activities and climate change on river runoff(Masih et al., 2009; Samadi et al., 2012). A number ofstudies have defined climate change scenarios based on theeffects of human activities on land use change andcombined the climate change scenarios with hydrologicalsimulation to analyse the variability of river runoffquantitatively (Ficklin et al., 2009; Montenegro andRagab, 2010). One approach distinguishes the effects ofhuman activities and climate change on river runoff bydefining a reference period with few human activities andan impacted period with intense human activities throughthe Pettitt–Mann–Whitney change-point statistics, trendtests, break point analysis and so on (Mann, 1945; Kendall,1975; Pettitt, 1979; Zhang et al., 2012). Physically basedhydrological models, such as the Soil and WaterAssessment Tool (SWAT) (Arnold et al., 1998), anddistributed hydrology soil and vegetation models have alsobeen adopted to study hydrologic responses to changes inclimate and to distinguish the effect of human activities(e.g. Jones et al., 2006; Liu et al., 2009). The effect ofclimate change on runoff variation is first estimated; afterwhich, the remaining effects were attributed to otherfactors such as human activities (Zhao et al., 2010).However, the effects of human activities and climatechange on runoff vary from place to place and need to beinvestigated at a local scale based on watershedproperties. In this study, we adopted a hydrologicalmodel with an improved method to quantify the relativeimportance of human activities and climate change onriver runoff using long-term observational data.The objectives of this paper are as follows: (1) to
simulate annual runoff based on 50 years of observationaldata in Jinghe River Basin, a typical arid inland basin innorthwest China, using the SWAT model and (2) toanalyse the relative importance of human activities andclimate change on the runoff in the basin using asequential cluster method, hydrological model simulationand separation approach.
STUDY AREA
Jinghe River, with a total length of 114 km, originatesfrom the Po Luo Kenu Mountains and flows from theconvergence of the Dongdujinghe and Wutujinghe Riversto north through Jinghe County and into the Ebinur Lake.Jinghe River Basin covers an area of 2150 km2 (82°02′Eto 83°57′E, 44°00′N to 44°50′N) (Figure 1).Jinghe River Basin belongs to temperate continental,
arid and semi-arid zones. The climate has the followingcharacteristics: low rainfall, strong evaporation and highseasonal and yearly variation in rainfall. The runoffsource is mainly rainfall. Glaciers and snow ablation are
also primary recharge sources of the river in the Po LuoKenuMountains. The average annual runoff is approximately4.761� 108m3 at the Jinghe hydrological station. Theaverage annual runoff into Ebinur Lake is only1.686� 108m3 in recent years. With the increase in humanpopulation and economic development, the diversion of freshwater in the basin for economic activities, such as agriculturalirrigation, has increased over the past two decades. Thisphenomenon has significantly decreased river runoff.This study divided the whole basin into two parts (i.e.
upstream and downstream) through the Jinghe hydrolog-ical station to examine whether human activities andclimate change affect runoff differentially between theupstream and downstream. The upstream and downstreamof Jinghe River have lengths of 75.2 and 38.8 km,respectively. The watershed areas of the upstream anddownstream are 1419 and 731 km2, respectively.
MODEL DESCRIPTION
Soil and Water Assessment Tool, which is a distributedhydrological model developed by the United StatesDepartment of Agriculture, has been widely used for waterresource simulation (Arnold et al., 1998). SWATmodel hasthe advantage of computational efficiency for its use ofHydrological Response Units (Neitsch et al., 2005). In thisstudy, the AVSWAT version of SWAT model was used tosimulate annual runoff. This version integrates theSWAT2005 model with ArcView (Luzio et al., 2005). InAVSWAT, data are pre-processed with the ESRI ArcViewGIS functionalities.
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EFFECTS OF HUMAN ACTIVITIES AND CLIMATE CHANGE ON RIVER RUNOFF
DATA AND METHODS
Data sources
Data on the river runoff and irrigation area between 1957and 2008 were obtained from the Hydrology and WaterResource Survey Bureau in Bole of Xinjiang. Meteorolog-ical data, such as annual precipitation (1957–2008), annualaverage air temperature (1957–2008) and annual averageevaporation (1959–2008), were obtained from the Jinghehydrological and weather stations. Land use and soil typemaps (1:1 000 000) were obtained from the XinjiangEnvironmental Protection Technique Consultation Center,and the Digital Elevation Model (30� 30m in resolution)was obtained from the Xinjiang Weather Bureau RemoteSensing Center.
Sequential cluster method
The hydrological sequence significantly affected byhuman activities differs from the original natural sequence.These two sequences were regarded as two categories. Asequential cluster method can be used to identify thehydrological stages of different characteristics, i.e. periodswith few human activities (the reference period) and thosewith intense human impacts (the impacted period), byidentifying the optimal breakpoint of the watershedhydrological sequence. This method deduces the optimalbreakpoint between periods using the following equations(Wang and Jia, 2001):
V τ ¼ ∑ αi � ατð Þ2 (1)
Vn�τ ¼ ∑ αi � αn�τð Þ2 (2)
where τ is the breakpoint corresponding to theminimum value determined using Equation (3), n isthe number of years in the hydrological sequence, αi isthe value in year i, ατ is the average of the hydrologicalsequence before τ, αn�τ is the average of thehydrological sequence after τ, Vτ is the sum of thesquared deviation before τ and Vn� τ is the sum ofthe squared deviation after τ.The difference in the sum of the squared deviation
before and after τ was calculated as
Sn τð Þ ¼ V τ � Vn�τ (3)
where Sn(τ) is the difference. When Sn∇(τ) =min[Sn(τ)], the
time point τ is the optimal breakpoint.
Mathematical–statistical methods
The single sample Kolmogorov–Smirnov test is amethod that utilizes sample data to infer whether toobey a theoretical distribution (Yang and Zhang,2001). This method is applicable to exploring the
distribution pattern of a continuous random variable.Thus, this method was adopted in this study toexamine the variations of air temperature, precipitation,runoff and irrigation area over the past 50 years. Eachvariable was divided into two independent samplesaccording to the time of abrupt change point. Thesignificance level of the four variables was analysedusing the independent sample t-test (including twosteps: Levene’s test and t-test). Moreover, the corre-lations between temperature and runoff as well asprecipitation and runoff were examined using correla-tion analysis. These processes were performed usingSPSS 16.0 version.
Hydrological model simulation
Hydrological simulation method is generally adoptedto simulate the runoff processes. The application of thismethod requires representative hydrological andmeteorological data, and the selected hydrologicalmodel should be capable of solving the key problemin the study area. The effects of snowmelt and frozensoil on the hydrological cycle were considered in thestructure of SWAT model, which is more suitable forthe alpine area of northwest China (Huang and Zhang,2004; Wang and Chen, 2007).Sensitivity analysis: The distributed models used in
hydrological modelling have numerous parameters.Parameter uncertainty can significantly affect thesimulation results. To ensure efficient calibration, thesensitivity of model parameters must be assessed (Chu,2003). The relative sensitivity can be expressed as(Nearing et al., 1990)
S ¼ M2 �M1ð Þ=M12
I2 � I1ð Þ=I12 (4)
where S is the relative sensitivity of the parameters, I1and I2 represent the minimum and maximum valuesused to calibrate the parameters, respectively, I12 is theaverage of I1 and I2, M1 and M2 are the simulated valueof parameters I1 and I2, respectively and M12 is theaverage of M1 and M2. The greater the absolute valueof S is, the more sensitive the parameter.
Calibration and verification analyses: SWAT modelcontains numerous difficult-to-measure parameters.Calibration is the process of adjusting the modelparameters, initial and boundary conditions andlimiting conditions to make the simulation resultsclose to the measured value. To examine the modelvalidity, the relative error, linear regression coefficientsand Nash–Sutcliffe efficiency coefficient were referredto evaluate the fitting between the simulated andobserved values.
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W. DONG ET AL.
The relative error is given as
RE ¼ Qg � Qm
Qg� 100% (5)
where Qg is the observed value, Qm is the simulated valueand RE is the relative error.The correlation coefficient R2 was obtained using linear
regressions in Excel. The Nash–Sutcliffe efficiencycoefficient E is given as (Nash and Sutcliffe, 1970)
E ¼ 1�∑n
i¼1Qg � Qm
� �2
∑n
i¼1Qg � Qavg
� �2(6)
where Qg is the observed value, Qm is the simulatedvalue, Qavg is the average of the observed values andn denotes the total number of the observed data.
00.010.020.030.040.050.060.070.080.09
1957 1962 1967 1972 1977 1982 1987 1992 1997 2002
Figure 2. Sum of squares of the annual runoff coefficient in study area
Separation approach of human activity and climate changeeffects on runoff
Compared with the average runoff in the referenceperiod, the runoff variation in the impacted periodconsists of two parts: the effect of climate change andthat of human activities. In the separation approach ofhuman activity and climate change effects on runoff, wefollowed previous studies (Cui, 2004) to conduct theseparation analysis. Runoff was expressed using runoffdepth (unit: millimetre) in this study.The separation approach can be described by the
following equations:
WT ¼ WN �WL (7)
WC ¼ WM �WN (8)
WH ¼ WT �WC (9)
ηH ¼ WH=WT � 100% (10)
ηC ¼ WC=WT � 100% (11)
whereWT is the total runoff variation, i.e. the difference ofthe average runoff in the reference period and the averagerunoff of the watershed outlet in the impacted period,WN isthe average runoff in the reference period, WL is theaverage runoff of the watershed outlet in the impactedperiod, WC is the runoff variation attributed to climatechange, i.e. the difference of the simulated ‘nature’ runoffin the impacted period and the natural runoff in thereference period,WM is the simulated ‘nature’ runoff in theimpacted period by SWAT model based on the calibratedparameters, WH is the runoff variation attributed to humanactivities, i.e. the difference of the total runoff variation and
the runoff variation attributed to climate change and ηHand ηCmeasure the relative impacts of human activities andclimate change on runoff, respectively.
RESULTS
Abrupt change point and statistical test
A sequential cluster method was adopted in this study todetect the change points in runoff series. The squareddeviation of the annual runoff coefficient was obtained todetermine the breakpoint when human activities began toaffect runoff substantially and showed an evident abruptchange around 1981 (Figure 2). The evolution of riverrunoff can be divided into two stages as follows: 1957 to1981 and 1981 to 2008.In the study basin, human activities reflect a land use
change and water demand increase. Land use changeevidently increases in cultivated land (i.e. irrigation area).The single sample Kolmogorov–Smirnov test results showthat the distribution patterns of temperature, precipitation,runoff and irrigation area were in normal distribution (e.g.runoff, see Figures 3 and 4). The t-test results indicate thatthe changes in these four variables were statisticallysignificant (α= 0.05) before and after 1981(Table I).According to the meteorological data at the hydrologicalstation of the Jinghe River Basin, the correlation betweenannual precipitation and runoff was significant at the 0.01confidence level, whereas that between temperature andrunoff was not significant (Table II).
Model calibration and evaluation
Soil and Water Assessment Tool model was calibratedusing the river annual runoff data collected from 1960 to1969. The automatic calibration analysis was conductedusing the automatic calibration options in SWAT 2005(Griensven, 2005). On the basis of the parametersensitivity analysis, CN2 is the most sensitive parameter,whose sensitivity was between 0.5 and 1. This valuebelongs to greater sensitivity. The parameters followingCN2 were ESCO, SOL_AWC, EPCO and GWALPHA,
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Figure 3. The normal distribution histogram of runoff from 1957 to 1980in Jinghe River Basin
Figure 4. The normal distribution histogram of runoff from 1981 to 2008in Jinghe River Basin
EFFECTS OF HUMAN ACTIVITIES AND CLIMATE CHANGE ON RIVER RUNOFF
whose sensitivity were all between 0.25 and 0.5,belonging to general sensitivity (Chen et al., 2011).These parameters were mainly associated with soil wateror groundwater processes. They were identified for themodel automatic calibration. All other parameters wereset to their default values. More detailed parameterdefinitions are given in Table III.The statistical evaluation of SWAT performance was
conducted using the observed and simulated values of theriver annual runoff from 1970 to 1980. The resultsindicate that Nash–Sutcliffe efficiency coefficient was0.92 in the calibration period and 0.89 in the validationperiod. The average relative errors between the simulatedand observed values were only 3.1% and 3.42% in thecalibration and validation periods, respectively (Tables IVand V). The correlation coefficients R2 were 0.92 and
0.90 in the calibration and validation periods, respective-ly. These results show that the model has high simulationaccuracy (Qi and Grunwald, 2005).
Runoff variation in the upstream and downstream areas
The means and standard deviations of the annualrunoff in the upstream and downstream areas wereanalysed to better understand the characteristics ofrunoff variation. Both areas experienced high runoff atdifferent times (e.g. 1998 and 2002), which can beattributed to long duration and frequent heavy rainfallevents. An extreme flood event occurred in 2002 owingto the accumulation of affluent runoff from all branches,which resulted in the maximum water quantity for thewatershed outlet (12.226� 108m3) in the past decades(data were obtained from the Hydrology and WaterResource Survey Bureau in Bole of Xinjiang). Comparingthe observed annual runoff in the upstream area with thatof the downstream area since the 1980s, a greater changein runoff occurred in the 1980s, in which the yearlyaverage reduction was 271.8mm. The yearly averagereduction in runoff decreased in the 1990s at 240.7mm.Relative to the average runoff (4.6414� 108m3/a) in the1970s, the average increases in the upstream area were0.3574� 108, 0.1665� 108 and 0.4728� 108m3/a in the1980s, 1990s, and 2000s, respectively. These increasesare consistent with the change in temperature (Figure 5).However, the ratio of runoff reduction in the downstreamarea to the upstream area has reached 60% since 1980s.
Relative effects of human activities and climate change onrunoff variation
The period between 1981 and 2008 was considered as‘the impacted period’. Compared with ‘the referenceperiod’, the average annual runoff reduction induced byhuman activities in the 1980s, 1990s and between 2000and 2008 accounted for 87.0%, 82.9% and 87.1% of thetotal reduction, respectively. The reductions caused byclimate change were only 13.0%, 17.1% and 12.9%,respectively (Table VI). The economy of the JingheRiver Basin mainly depends on agriculture. Economiccrops, such as cotton, are planted in the basin. To someextent, the increase in the sown area of the economiccrops determines the trends in actual water consumption.With the oasis expansion and intensified agriculturalexploitation, man-made water channels and facilitieshave changed the pathways of surface water and affectedthe annual surface runoff significantly. This effect wasgreater in the 1980s and between 2000 and 2008(Figure 6). By contrast, precipitation and temperatureexhibited a slowly decreasing tendency in the down-stream area since the 1980s (Figure 7), suggesting lesscontribution to the downstream runoff reduction.
SWAT, Soil and Water Assessment Tool.*The range values indicate that the minimum and maximum parameter values represent the range for the entire watershed.
Table II. Correlations of the precipitation, temperature and runoff for this study
**Correlation is significant at the 0.01 level (2-tailed).
EFFECTS OF HUMAN ACTIVITIES AND CLIMATE CHANGE ON RIVER RUNOFF
DISCUSSION
Since the 1980s, data from the Jinghe hydrological stationindicate a warming trend in Jinghe River Basin. The dataanalysis results show that the temperature rise mostoccurred from 2000 to 2008 (the average is about 2 °Cgreater than that of in the 1980s). The averageprecipitation in the 1990s decreased to 22.48 mmcompared with that of in the 1980s. At the same period,the increase in temperature is greater at the Jingheweather station than at the Jinghe hydrological station,whereas the precipitation showed an opposite trend. Theelevation of the former was 321.2m near the residentzone, whereas that of the latter was 619m at thewatershed mountain pass, which indicates differentregional climate changes within the basin.Climate change causes decreases in runoff in some
regions of the world but increases in others such as East andSouth Asia (Arnell, 2004). The relevance between climatechange variables (e.g. temperature and precipitation) and
hydrological variables (e.g. river runoff) has been analysedand reported (Burn, 2008; Masih et al., 2010). This researchshows that climate change also increases the runoff inupstream of the Jinghe River Basin, where the runoffvariation is directly related to the precipitation andtemperature. This finding is consistent with those of Wanget al. (2012) and Xu et al. (2011), who found that the annualrunoff in the Aksu River, northwest China, has a positivecorrelation with temperature and precipitation. In addition,the correlation between the annual runoff and precipitationis larger than that between the annual runoff and temperaturein the Jinghe River Basin, where the average annualprecipitation and temperature were approximately212.5mm and 7.57 °C in ‘the reference period’ andincreased to approximately 242.6mm and 7.91 °C during‘the impacted period’, respectively. Hence, the increase ofprecipitationmight lead to an increase in runoff. Zhang et al.(2009) suggested that the runoff variation in the TarimRiverBasin, northwest China, strongly depended on the increases
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Figure 5. Inter-annual change of climate factors and runoff in Jinghehydrological station
Table V. Simulation results for the validation period from 1970to 1980
Figure 6. Relative effects of human activities and climate change onannual runoff
Figure 7. Inter-annual change of climate factors in Jinghe weather station
EFFECTS OF HUMAN ACTIVITIES AND CLIMATE CHANGE ON RIVER RUNOFF
in precipitation and glacier melting in recent decades,particularly after the 1980s. The runoff contribution in theupstream of the Jinghe River Basin has mainly depended onthe increase of precipitation since the 1980s. The temper-ature rise influences precipitation process, increases glaciermelting and affects runoff generation. These researchessuggest that the increasing trend in the upstream annualrunoffmight be attributed to the increase of precipitation andsnowmelt.Human activities are also dominant influence factors for
runoff variation. Climate change usually impacts runoffvariation periodically and lastingly, whereas humanactivities impacts runoff variation suddenly anddirectionally (Miao et al., 2011). The upstream riverrunoff increased over the past decades, implying that therunoff reduction is caused by human activities. Runoffprocess is directly and strongly interfered by humanactivities (Konrad and Booth, 2002; Olivera and DeFee,2007). This interference has also been reported inliteratures (Cox et al., 2006; Peel, 2009). However, therelative effects of human activities and climate change onrunoff variation require further analysis. In someundeveloped regions, improving the current understandingof runoff variation is difficult because of a lack of data.Hydrologic model simulation and separation approach canbetter solve the problem (Cui, 2004). The key of thesemethods is to determine the reference and impacted periodsand the selection of a hydrological model.Identification of the breakpoint of the two periods is
one of the most important statistical techniques for river
runoff variation analysis to investigate the impacts ofclimate change and human activities. A trend and change-point analysis method is often used in the similarresearches (Kiely et al., 1998; Matouˇskov´a, 2009). Thisapproach considers a time series (precipitation or runoff)as two samples and then analyses the change of these twoseries. This change in the gradient of the curve may inferthat the characteristics of precipitation or runoff havechanged. However, in this research, the change of riverrunoff series was analysed by calculating the long-termannual runoff coefficient, which can reduce the uncer-tainty of such inference. An improved approach was thenadopted to separate the relative effects of climate changeand human activities on runoff variation. Compared withthe methods used by Seguis et al. (2004) and Franczykand Chang (2009), the method adopted in this researchconsiders more regional characteristics, e.g. the spatialdistribution characteristics of river runoff and influencingfactors, and is not purely a regression analysis or multi-regression (Heiazi and Moglen, 2007; Jiang et al., 2011).Thus, the analysis results can contribute to the similarbasins. This approach can be further improved.The relative impacts of human activities and climate
change on the annual runoff in the basin can bedetermined on the basis of the statistical test results.The ‘natural’ runoff in the impacted period was simulatedusing SWAT model based on the calibrated parameters.According to model sensitivity analysis results, SWATmodel is sensitive to its soil parameterization, which is inagreement with the results reported in literatures (e.g.Muttiah and Wurbs, 2002; Romanowicz et al., 2005). Inthe case of other catchments, where different land use andtopographical pre-processing is considered, this modelmay show different results (Chess Project, 2001; Yanget al., 2008). In this study, the simulation performance ofthe watershed runoff is higher and the percent errors aresmaller than those of the similar studies, where theaverage annual Nash–Sutcliffe efficiency coefficient was0.8 to 0.9 (e.g. Fontaine et al., 2002; Wu and Johnston,2007). Both calibration and validation results indicate thatthe annual runoff simulated obtained using SWAT modelbetter agrees with the observations in the study area. Theresults show that the SWAT model is suitable andacceptable in the hydrological simulation in an arid basin.With the intensification of human activities, such as
land-use change, wasteland reclamation and irrigationwater diversion, the conflicts between the supply of anddemand for water resources are becoming a severeproblem across the entire basin (Dong and Liu, 2010).The impact intensity of human activities is mainlyattributed to economic factors related to agriculturalexploitation and the population. From 1981 to 2008, asignificant increase was detected in the irrigation area,with a significance level of 0.05. The eco-environmental
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W. DONG ET AL.
problems mainly derived from the decreased river runoffare mostly attributed to human activities, which are moredifficult to resolve rapidly and effectively. Only onereservoir can be found in the study area. This reservoirwas designed for water and flood control but its functionand effectiveness were not achieved in recent years owingto ineffective management. The spatial and temporalpatterns in the runoff must be manipulated by developingan optimized reservoir operation rule. This rule couldreduce the risks of drought and flood and meet the waterdemands for humans, with the increase of economicactivities and severity of climate change.
CONCLUSIONS
Human activities and climate change can significantlyaffect the runoff in arid and semi-arid regions. In thisstudy, we estimated the relative impacts of humanactivities and climate change on the runoff variation inJinghe River Basin based on long-term observationaldata by SWAT model, combined with sequentialcluster method and improved separation approach.Our conclusions are as follows:
1. The runoff reduction between 1981 and 2008 causedby human activities accounted for 85.7% of the totalreduction in the downstream area, whereas climatechange accounted for only 14.3% of the downstreamrunoff reduction, suggesting that human activities arethe major drivers of the runoff variation in the basin.
2. The intense human diversion of fresh water forirrigation has resulted in the runoff reduction in thedownstream since 1981, which was determinedthrough significant test. For the sustainable develop-ment of the entire Jinghe River Basin, particularlythe lower reaches, reducing the impacts of humanactivities (especially the irrigation area) and rationalallocation of water resources are critical.
3. Quantifying the impacts of human activities and climatechange on runoff will contribute to regional waterresources assessment and management. In the futurework, we will optimize the uncertainty factor of themodels, including the improvement of the modelstructure and strengthening of the different impacts ofdifferent seasons on the sensitivity of the modelparameters.
ACKNOWLEDGEMENTS
Special thanks to Dr He Qiang and Ms Hua Yanyan ofBeijing Normal University for their comments on thismanuscript. This research is financially supported by
China National Funds for Distinguished Young Scien-tists (51125035), the National Science Foundation forInnovative Research Group (51121003), National NaturalScience Foundation (50809005), and Key NationalNatural Science Foundation (41130531).
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