quantitative assessment of climate and human impacts on...

INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. (2014)Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/joc.3965

Quantitative assessment of climate and human impactson surface water resources in a typical semi-arid watershedin the middle reaches of the Yellow River from 1985 to 2006

Zhidan Hu,a* Lei Wang,b Zhongjing Wang,a Yang Hongc,d,e and Hang Zhenga

a State Key Laboratory of Hydro-science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, Chinab Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research,

Chinese Academy of Sciences, Beijing, Chinac Department of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, USA

d Hydrometeorology and Remote Sensing Laboratory, University of Oklahoma, Norman, OK, USAe Advanced Radar Research Center, University of Oklahoma, Norman, OK, USA

ABSTRACT: The surface water resources of a typical semi-arid watershed (Huangfuchuan) in the middle reaches ofthe Yellow River have drastically decreased over the past decade, which has affected the governance strategies of theentire Yellow River. The causes of the decrease in surface water are generally attributed to climate fluctuations andhuman activities. In this study, a distributed biosphere hydrological model the Water and Energy Budget-based DistributedHydrological Model (WEB-DHM) and a Contribution Assessment method were jointly applied to diagnose and quantifyclimate and human impacts on the streamflow change. Long-term hydrometeorological trends were analysed first and onemajor change-point (in 1998) in the annual streamflow series was identified through the nonparametric Mann–Kendalltest and the annual precipitation-streamflow double cumulative curve method. The WEB-DHM model was calibrated andvalidated over the baseline period of 1985–1998; the natural streamflow was reconstructed for the impacted period of1999–2006. Then, the contributions of climate fluctuations and human activities to streamflow change were determinedquantitatively by comparing the natural streamflow with the observed value. The mean annual streamflow significantlydecreased from 34.05 mm year−1 to 13.72 mm year−1 in the baseline and impacted periods, respectively, showing a reductionof 60%. Climate fluctuations accounted for a decrease in mean annual streamflow of approximately 10.38 mm year−1

(51.03%), whereas human activities (including soil–water conservation measures, artificial water intakes and man-madewater storage infrastructure) caused a decrease of approximately 9.96 mm year−1 (48.97%). These findings are potentiallyhelpful to support the water resources planning and management in the middle reaches of the Yellow River.

KEY WORDS climate fluctuations; human activities; surface water resources; distributed biosphere hydrological model;semi-arid Huangfuchuan River Basin

Received 25 March 2013; Revised 17 December 2013; Accepted 27 January 2014

1. Introduction

Climate fluctuations and human activities have a pro-found impact on various elements of the hydrologic cycle(Ohmura and Wild, 2002; Barnett et al., 2008; Conget al., 2009). Specifically, the impact on surface waterresources (Wang and Hejazi, 2011; Zhang et al., 2012)has attracted widespread attention and concern all overthe world. Studies indicate that the mean annual globalsurface temperature has increased by 0.74 ◦C in the past100 years (1906–2005) (IPCC, 2007). Surface tempera-ture increase leads to higher evaporation rates and enablesmore water vapour transportation, therefore, accelerat-ing the global hydrologic cycle (Menzel and Burger,2002). Because of the redistribution of precipitation and

* Correspondence to: Z. Hu, State Key Laboratory of Hydro-scienceand Engineering, Department of Hydraulic Engineering, Tsinghua Uni-versity, Beijing, 100084, China. E-mail: [email protected]

the change in temperature, climate fluctuations have adirect influence on the amount of water available (Mid-delkoop et al., 2001; Yang et al., 2004). Additionally,human activities can also bring variability to the watercycle and affect the spatial and temporal patterns of waterresources (Zhang et al., 2013). Cultivation, urbanization,and other such human activities mainly affect basin-and region-scale hydrologic processes via land use andland cover change (DeFries and Eshleman, 2004; Zhangand Schilling, 2006; Thanapakpawin et al., 2007; Hamdiet al., 2011) and thus have an indirect effect on stream-flow. In contrast, water resource withdrawal (Ma et al.,2010) and return flow (Wang and Cai, 2010), hydraulicconstruction and operation (Batalla et al., 2004), andother anthropogenic modifications (Arrigoni et al., 2010)have a direct impact on the availability of water resourcesby altering their spatiotemporal distribution. In a broadsense, human activities can also alter the hydrologic cycleby disturbing correlated climate variables (Wang and

2014 Royal Meteorological Society

Z. HU et al.

Hejazi, 2011). However, from a practical point of view,the human-induced impact on climate is not examined inthis study.

The Yellow River, also known as the Huanghe inChinese, is considered China’s mother river and thecradle of Chinese civilization. It is an important watersource for hundreds of millions of people in the northernand north-western parts of China. However, due toclimate fluctuations and increased human activities (Conget al., 2009), water scarcity has become more severein recent years; the drying up of the main river wasparticularly aggravated in the 1990s (Zhang et al., 2009).For example, in recent decade, the amount of surfacewater resources in the Huangfuchuan River Basin, a semi-arid watershed in the middle reaches of the Yellow River,was less than a quarter of that observed in the FirstNational Water Resources Assessment (around the end ofthe 1970s). Therefore, quantitative assessment of climateand human impacts on surface water resources (e.g.streamflow) is particularly important; it is the foundationof regional water resources planning, management, andsustainable development.

Traditionally, the contribution of human activitiesto streamflow was estimated via the subentry inves-tigation method (Wang et al., 2010b). However, anincrease in anthropogenic effects may introduce sub-stantial uncertainty to the assessment results due toproblems involving restoration distortion and invalida-tion (Wang et al., 2003). On the basis of regressionmethods, some researchers have tried to analyse thesensitivity of streamflow to variations in precipitation,evaporation, and other hydrometeorological variables toassess the impact of climate fluctuations (Dooge et al.,1999; Li et al., 2007; Jiang et al., 2011). Recently, asremote sensing and geographic information system tech-niques have improved, many hydrologists have attemptedto quantify these impacts separately and execute waterresources assessments through hydrological modellingmethod (Wang et al., 2010a; Bao et al., 2012; Zhanget al., 2012). Despite some limitations to the applica-tion of hydrological modelling, such as the uncertain-ties arising from input data, model structure, and modelparameters, the data demanding and the trade-off betweensimulation accuracy and computational cost (Xu andSingh, 2004), hydrological modelling method is still goodapproach in quantifying effects individually and to assessthe water resources (Wang et al., 2010b). In particular,new-generation hydrological models with coupled waterand energy budgets are more promising in applicationsinvolving relatively dry conditions (e.g. arid or semi-aridriver basins; see Wang et al., 2011), when compared withtraditional water-balance models.

The objective of this study is to quantitatively analysethe impacts of climate fluctuations and human activitieson the decreasing streamflow in the semi-arid Huang-fuchuan River Basin in the middle reaches of the YellowRiver from 1985 to 2006. We analyse the hydrome-teorological trends and change-points and then simu-late the natural hydrologic processes for a period of 22

consecutive years, using a calibrated biosphere hydro-logical model. On the basis of observations and hydro-logical simulations, the contributions of climate fluctu-ations and human activities to the streamflow changesare quantitatively determined. This study is potentiallyvaluable and practical for improving the understandingof the hydrologic cycle and promoting water resourcesplanning and management in the middle reaches of theYellow River.

2. Methodology

2.1. Trend and change-point analysis methods

The nonparametric Mann–Kendall (MK) test (Mann,1945; Kendall, 1975) was applied to detect trends andidentify change-points of precipitation, mean tempera-ture, and streamflow in the Huangfuchuan River Basin.This methodology can handle non-normalities with highasymptotic efficiency (Berryman et al., 1988), and it iswidely used for the analysis of trends in various hydro-meteorological series (Zhang et al., 2009; Jiang et al.,2011). For a time series X = {x1,x2, . . . ,xn} (n > 10), theMK test statistic Z is calculated as follows (Xu et al.,2003):

Z =

S−1√var(S )

, S > 0

0 S = 0S+1√var(S )

, S < 0

, (1)

in which

S =n−1∑i=1

n∑k=i+1

sgn (xk − xi ), (2)

where sgn(θ) is equal to 1, 0, or −1 when θ isgreater than, equal to, or less than 0, respectively. Thenull hypothesis, H0, stands that there is no statisticallysignificant trend in the series. The H0 is accepted if|Z | ≤ Z 1 − α/2, where Z 1 −α/2 is the 1 − α/2 quantile of thestandard normal distribution for a given significance levelα. Otherwise, the H1 hypothesis is accepted, and the trendis statistically significant. A positive Z value denotesan increasing trend, and the opposite demonstrates adecreasing trend.

To determine the occurrence of a change-point in a dataseries, the test statistic (UFi ) is estimated by the flowingformulas (Zhang et al., 2007; Bao et al., 2012):

UFi = Si − E (Si )√var (Si )

(i = 1, 2, . . . , n) , (3)

Sk =k∑

i=1

ri (k = 2, 3, . . . , n) , (4)

ri ={

1, xi > xj

0, xi ≤ xj(j = 1, 2, . . . , i − 1) . (5)

Because x i is an independent and identically dis-tributed random variable, the expected value E (S i ) and

2014 Royal Meteorological Society Int. J. Climatol. (2014)

LONG-TERM WATER RESOURCES IN MIDDLE YELLOW RIVER

variance var(S i ) can be given as follows:

E (Si ) = i (i − 1)

4, (6)

var (Si ) = i (i − 1) (2i + 5)

72. (7)

Then, by the calculation of UFi for the inversetime series xn , xn − 1, . . . , x1 again and the definition ofUBi = − UF i , i = n , n − 1, . . . , 1, the curve of UFi andUBi can be plotted. If a match point of the two curvesexists and the series trend is statistically significant, thematch point can be regarded as a change-point of theseries with high probability.

2.2. Contribution assessment of the climate and humanimpacts on surface water resources

For the purpose of separating and quantifying the climate-and human-induced impacts on streamflow variation,the hydrological model simulation method was adoptedin this study, along with the hypothesis that climatefluctuations and human activities are independent (Wanget al., 2008). Through trend and change-point analysis,the whole streamflow series can be divided into a baselineperiod series and an impacted period series. The observedstreamflow change between the two periods demonstratesthe combined influences of climate fluctuations andincreased human activities (Bao et al., 2012), which canbe expressed as:

�QT = �QC + �QH = QOI − QB , (8)

where the total change of the annual streamflow (�QT )includes two parts: the streamflow change caused byclimate fluctuations (�QC ) and human activities (�QH ),whereas QB and QOI are the observed mean annualstreamflow in the baseline period and the impactedperiod, respectively.

Later, a hydrological model is calibrated in the base-line period and then forced by meteorological data tosimulate natural streamflow in the impacted period (QSI ),without consideration of local human activities; this cantherefore be regarded as the hydrologic response to cli-mate variation only (Wang et al., 2010a). In this way,the difference between the reconstructed mean annualstreamflow and baseline value which is the observedmean annual streamflow in the baseline period can beconsidered as representing the influence of climate fluc-tuations on streamflow change (Wang et al., 2008).

�QC = QSI − QB (9)

Finally, the contribution of climate fluctuations andhuman activities to streamflow change, which are definedas ηC and ηH , respectively, are quantitatively estimatedby:

ηC = �QC

�QT× 100%, (10)

ηH = �QH

�QT× 100%. (11)

2.3. WEB-DHM model

As a distributed biosphere hydrological model, WEB-DHM (the Water and Energy Budget-based DistributedHydrological Model; Wang et al., 2009a, 2009b, 2009c)is developed by fully coupling a simple biospherescheme SiB2 (Sellers et al., 1996a) with a hillslope-basedhydrological model, Geomorphology-Based HydrologicalModel (GBHM) (Yang et al., 2002). It has the ability toconsistently describe water, energy, and CO2 fluxes in abasin. The model has been applied to simulate discharge,fluxes, land surface temperature (LST), and surface soilmoisture in multiple time and space scales in several riverbasins (Wang et al , 2009b; Jaranilla-Sanchez et al., 2011)with reliable accuracies, including in semi-arid environ-ments (Wang et al , 2011). As illustrated in Figure 1, theoverall structure of WEB-DHM can be described as fol-lows:

(1) A digital elevation model (DEM) is used to define theresearch basin and then it is divided into sub-basins(see Figure 1(a)). With regard to each sub-basin, flowintervals are specified to represent time lags and theaccumulating processes in the river network. Eachflow interval is composed of several model grids (seeFigure 1(b)).

(2) For each model grid, there is one combination ofland use type and soil type. Here, the land surfacesubmodel is used to independently calculate thetransfer of turbulent fluxes between the atmosphereand land surface (see Figure 1(b) and (d)). Thevertical water distributions for all the model gridscan be obtained through this biosphere process.

(3) Each model grid is subdivided into a number of geo-metrically symmetrical hillslopes (see Figure 1(c)).In WEB-DHM, a hillslope with unit length is nameda Basic Hydrological Unit (BHU). Within a givenBHU, the hydrological submodel is used to sim-ulate lateral water redistribution and to calculaterunoff consisting of overland, lateral subsurface, andgroundwater flows (see Figure 1(c) and (d)). The totalresponse of all BHUs within a model grid comprisesthe runoff for a grid cell.

(4) Simplifications have been made in which streamslocated in one flow interval are lumped into asingle virtual channel. All of the flow intervals areconnected by the river network generated from theDEM. All runoff from the grid cells in the given flowinterval accumulates in the virtual channel directedtoward the outlet of the river basin. The flow routingfor the entire river network in the basin is simulatedusing the kinematic wave approach.

In this study, the LST can be estimated by the WEB-DHM following Wang et al. (2009b).

Tsim =[V × T 4

c + (1 − V ) × T 4g

]1/4, (12)

V = LAI/LAImax, (13)


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Datum

Inter flowGroundwater table

Impervious Surface

Hillslope Unit

l

River

Precipitation

Soil surface

CO2H

Rlw

λET

Surface flow

Rsw

Grid size in the model

DEM grid size

(d)(c)

(b)(a)

2

1

3 5

4

7

6

9

8

Outlet

FlowIntervals

Subbasin

Groundwater flow

Figure 1. Overall structure of WEB-DHM model: (a) division from a basin to sub-basins; (b) subdivision from a sub-basin to flow intervalscomprising several model grids; (c) discretization from a model grid to a number of geometrically symmetrical hillslopes, and (d) processdescriptions of the water moisture transfer from atmosphere to river. Here, Rsw and Rlw are downward shortwave radiation and longwave

radiation, respectively; H is the sensible heat flux; and λ is the latent heat vaporization.

where T sim is the simulated LST; V is green vegetationcoverage; T c and T g are the temperature of the canopyand the soil surface, respectively; LAI is the leaf areaindex and LAImax is the maximum LAI values derivedfollowing Sellers et al. (1996b).

3. Datasets

3.1. Study region

Huangfuchuan River Basin originates in Southern InnerMongolia and encompasses the area from the south-eastern part of Erdos Plateau to the northern edge ofLoess Plateau. It covers longitudes from 110.33◦E to111.25◦E and latitudes from 39.20◦N to 39.99◦N (seeFigure 2(b)), with a catchment area of 3186 km2. Char-acterized by a semi-arid continental climate, the basin’saverage precipitation and mean temperature from 1961

to 2000 were 388 mm and 7.5 ◦C, respectively (Xu et al.,2011). The annual precipitation shows high temporalvariability; 76% falls between June and September, and53% falls in July and August. Consequently, the temporaldistribution of runoff is uneven, and runoff that occurs inthe flood season (June to September) accounts for 82.6%of the annual amounts. Moreover, as a seasonal river, theflow characteristics are representative and typical: highpeak discharge, short flood duration, and rapidly risingand falling flood speeds.

3.2. Available data

The input datasets for the Huangfuchuan River Basinused in WEB-DHM are described below.

Precipitation data recorded at 13 rainfall gauges from1985 to 2006 were provided by the Hydrological Bureauof Yellow River Conservancy Commission (YRCC).Hourly precipitation data were only available in the flood



Huangfu

Shagedu

111°0’0"E

111°0’0"E

110°30’0"E

110°30’0"E

40°0

’0"N

40°0

’0"N

39°3

0’0"

N

39°3

0’0"

N

39°0

’0"N

39°0

’0"N

LegendBasin BoundaryJungar BannerFugu countyRiverDischarge GaugeRainfall Gauge 0 10 205 Km

(b)

(a)

Hejin

Lijin

Huaxian

LongmenLanzhou

Hekouzhen

Huayuankou

Longyangxia

120°0’0"E

120°0’0"E

115°0’0"E

115°0’0"E

110°0’0"E

110°0’0"E

105°0’0"E

105°0’0"E

100°0’0"E

100°0’0"E

95°0’0"E

95°0’0"E

40°0

’0"N

40°0

’0"N

35°0

’0"N

35°0

’0"N

Legend

Basin

Yellow River

Discharge Gauge

0 300 600150 Km

Figure 2. The Huangfuchuan River Basin: (a) location within theYellow River Basin and (b) the locations of hydrometeorological

stations in the basin.

season. In the non-flood season, daily observations wereavailable and were downscaled to hourly data using theprecipitation duration (Wang et al., 2010b).

Meteorological data were extracted from the ChinaMeteorological Forcing Dataset (http://westdc.westgis.ac.cn/data/7a35329c-c53f-4267-aa07-e0037d913a21; seeHe, 2010; Yang et al., 2010; Chen et al., 2011). Thisdataset includes air temperature, air pressure, specifichumidity, wind speed, precipitation rate, and downwardshortwave and longwave radiation, with a spatial andtemporal resolution of 0.1◦ and 3-h, respectively. Thesemeteorological variables (except precipitation rate)were linearly interpolated to model grids (1000 m) forsimulations.

Geographical information used for the WEB-DHMmainly included topography, land use, soil type andvegetation (Figure 3). Digital elevation data used werethe NASA STRM (http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/SRTM), using approxi-mately 90-m resolution data resampled to a 1000-mDEM. The subgrid topography was described by a 25-mDEM, which was generated from 30-m ASTER GDEM(http://www.gdem.aster.ersdac.or.jp/index.jsp) and was

used to calculate the topographic parameters (hillslopelength and angle).

Land use types were reclassified to three SiB2 cate-gories, which were provided by Environmental & Eco-logical Science Data Center for West China, NationalNatural Science Foundation of China (http://westdc.westgis.ac.cn).

Soil type and hydraulic characteristics, including satu-rated soil-moisture content, residual soil-moisture con-tent, and saturated hydrological conductivity for soilsurface and van Genuchten parameters (α and n) (vanGenuchten, 1980), were obtained from the Food andAgriculture Organization (FAO) global dataset (FAO,2003), with a 5-arc minute spatial resolution.

The vegetation static parameters, such as morpholog-ical, optical, and physiological properties, were definedfollowing Sellers et al. (1996b). The dynamic vegetationparameters are Leaf Area Index (LAI) and Fraction ofPhotosynthetically Active Radiation (FPAR) absorbed bythe green vegetation canopy. In this study, they wereobtained from the NOAA AVHRR PAL 16-km satellitedataset (Myneni et al., 1997) for the period 1985 to 2000and from the NASA MODIS MOD15A2 1-km products(Myneni et al., 2002) for the period 2001 to 2006.

Except for the input datasets, in situ and satelliteobservation data were used to evaluate the WEB-DHMperformance in simulating water and energy budgets(Wang et al., 2009b). Daily discharge data at two dis-charge gauges (Shagedu and Huangfu; see Figure 2) wasobtained from the Hydrological Bureau of YRCC. TheLSTs were obtained from the NASA MODIS MOD11A2V5 1-km 8-day product (Wan, 2008), which has beenavailable since 5 March 2000. A set of independent sta-tistical datasets from the Hydrological Bureau of YRCCand the Upper and Middle Yellow River Bureau werecollected and compiled to analyse the impact of humanactivities.

4. Results and discussion

4.1. Trends and change-point analysis forhydro-meteorological variables

A study period from 1985 to 2006 was selected, con-sidering the data availability and recorded streamflowchanges. Figure 4 plots the annual time series of precipi-tation, mean temperature, observed streamflow, and theirlong-term linear trends. Both precipitation and observedstreamflow show a decreasing trend, whereas the meantemperature shows a remarkable increasing trend. More-over, the precipitation and observed streamflow decreasedby 0.43% and 3.85% per year, respectively. The cli-mate of the basin became warmer and drier over thestudy period. Besides, the MK test results (Table 1)show the same trends as mentioned above. Specifically,the decreasing trend for observed streamflow and theincreasing trend for mean temperature are statisticallysignificant.


Z. HU et al.

LegendBasin Boundary

Dem1468

848

(a)


Slope(degree)14.20

1.40

(b)


Land Use TypeBroadleaf-Deciduous TreesShrubs with Bare SoilAgriculture/C3 Grassland

(c)


Soil TypeCAMBISOLSKASTANOZEMS

0 10 205 Km 0 10 205 Km

0 10 205 Km 0 10 205 Km

(d)

Figure 3. Spatial distribution of (a) DEM, (b) grid slope, (c) land use, and (d) soil type in the Huangfuchuan River Basin.

Table 1. Trend and change-point analysis for the annual precip-itation, mean temperature, and streamflow time series.

Factor MK test (α = 0.1)

Z H0 Chang-point analysis

Precipitation −0.11 A –Mean temperature 3.13 R 1996Streamflow −1.69 R 1998

R: reject H0; A: accept H0.

To investigate intra-annual variability, Figure 5presents the MK test results for seasonal hydromete-orological variables. Among them, the precipitationtrend varies seasonally, with a decreasing trend in JJAand SON and an increasing trend in MAM and DJF;however, none of the trends are statistically significant.Meanwhile, the MK test results show an increasing

trend for mean temperature and a decreasing trend forobserved streamflow in all seasons, which means agreater extent of warmer and drier conditions throughoutthe basin. The trends for evapotranspiration (ET),which is the difference between the precipitation andcontemporaneous streamflow, are consistent with theprecipitation. This implies that the water supply is thelimiting factor for ET other than the energy supply inthis basin.

Figure 6(a) demonstrates that one change-point isdetected in 1998 for the annual observed streamflowseries through the MK change-point test. Moreover,the annual precipitation-streamflow double cumulativecurve (Figure 6(b)) used as auxiliary material forchange-point detection shows that the relationshipbetween precipitation and streamflow has changedsince 1998. Because the results from both methods are



(a)

(b)

(c)

Figure 4. Time series of annual (a) precipitation, (b) mean temperature, and (c) observed streamflow with long-term linear trends (dashed line)from 1985 to 2006 in the Huangfuchuan River Basin.

Figure 5. Mann-Kendall’s testing statistic values (Z ) for seasonal (MAM, JJA, SON, and DJF) precipitation (P ), mean temperature (T ), observedstreamflow (Q), and evapotranspiration (ET ) which is the difference between precipitation and contemporaneous streamflow from 1985 to 2006

in Huangfuchuan River Basin.

consistent, 1998 is identified as the change-point withrelatively high probability, and a baseline period of1985–1998 and an impacted period of 1999–2006 aredistinguished from the whole series.

4.2. Natural streamflow reconstruction

4.2.1. Model calibration and verification

At a 1000-m spatial and hourly temporal resolution,the WEB-DHM model was first calibrated with dailydischarge at Huangfu station from 1985 to 1990. Sev-eral parameters were optimized through trial and errormethods by matching the simulated and observed daily

discharge at Huangfu station. The basin-averaged param-eters are described in Table 2.

To display the calibration results more clearly,Figure 7(a) illustrates the anomaly curves of theobserved and simulated daily discharge at Huangfu.The average daily hydrograph is calculated based onthe observed value from 1985 to 1990. It is shown thatthe WEB-DHM can reconstruct the fine temporal-scaledischarge processes (reproducing both the peak andbase flows) well, with a Nash-Sutcliffe coefficient ofefficiency (NSCE; Nash and Sutcliffe, 1970) equal to0.913 and a relative bias (RB; Wang et al., 2010b)equal to −7.75%. In addition, the simulated discharge


Z. HU et al.

(b)(a)

Figure 6. (a) Mann-Kendall’s testing statistic values (UFi ) and (b) precipitation-streamflow double cumulative curve for change-point analysisof annual observed streamflow in Huangfuchuan River Basin (1985–2006).

Table 2. Basin-averaged values of the parameters used in the Huangfuchuan River Basin.

Symbol Parameters Unit Basin-averaged value Source

θ s Saturated volumetric moisture contentof unsaturated zone

m3 m−3 0.45 FAO (2003)

θ r Residual volumetric moisture contentof unsaturated zone

m3 m−3 0.07 FAO 2003)

α van Genuchten parameter 0.01 FAO (2003)n van Genuchten parameter 1.60 FAO (2003)anik Hydraulic conductivity anisotropy

ratio32.10 Optimization

K s Saturated hydraulic conductivity forsoil surface

mm h−1 3.77 Optimization

Dr Root depth (D1 + D2) m 1.02 Sellers et al. (1996b)

at Shagedu (Figure 7(b)) also agrees well with observedvalues, with NSCE of 0.927 and RB of −3.20%.

Using the same parameter values, the model wasthen validated from 1991 to 1998. The daily anomalyhydrographs at Huangfu and Shagedu (Figure 7(c) and(d)) show acceptable accuracy; the NSCE is equalto 0.675 and 0.706 and the RB is equal to 6.29%and −13.52%, respectively. These results confirm thegenerally good performance of the WEB-DHM in dailystreamflow simulation.

Figure 8(a) is the time series of monthly observed(Q _ obs) and simulated discharge (Q _ sim) for the upperarea of the Huangfu gauge from 1985 to 1998, withthe precipitation (P ) time series given for reference.Despite some differences at the peaks, there is fairlygood agreement between the simulated and observedstreamflow, with NSCE and RB values of 0.936 and−0.07%, respectively.

Generally, the calibration and verification resultsdemonstrate that the WEB-DHM can simulate daily andmonthly natural streamflow with good accuracy in theHuangfuchuan River Basin. The outputs of the calibratedWEB-DHM are reliable, and the model can be applied

to simulate natural streamflow series during the impactedperiod (1999–2006).

4.2.2. Natural streamflow reconstruction for theimpacted period

Without considering local human activities (e.g. land-use change), the benchmarked model was forced toreconstruct natural streamflow through the baseline andimpacted period. Figure 8 depicts the time series ofmonthly and annual observed and reconstructed stream-flow for the whole period. The verification period withthe decreased model performance may indicate intensifiedhuman impacts, and the significant differences betweensimulations and observations since the beginning of theimpacted period confirm this viewpoint (Wang et al.,2008).

Figure 9 gives the comparison of 8-daily LSTsbetween the WEB-DHM simulations (LST _ WEB -DHM) and MODIS observations (LST _ MODIS) atdaytime (around 10:30 hours at local time) and nighttime(around 22:30 hours at local time) averaged for the basinfrom March 2000 to December 2006. The results showthat the simulated LSTs agree well with the MODIS



(d)

(c)

(b)

(a)

Figure 7. Observed and simulated daily discharge anomaly curves in the calibration (1985–1990) and verification (1991–1998) periods at theHuangfu (a, c) and Shagedu (b, d) gauges.

LSTs except that some simulations are slightly overes-timated. The mean bias error (MBE; Wang et al., 2011)is equal to 0.19 K and 1.49 K and the root mean squareerror (RMSE) is equal to 2.83 K and 2.47 K during theday and at night, respectively (Figure 9(a) and (b)). Thescatter plots also show the good consistency betweenthe simulations and observations for both daytime andnighttime LSTs, with the correlation coefficient (R) being0.9826 and 0.9875, respectively (Figure 9(c) and (d)).

Figure 10 describes the seasonal changes of thespatial distribution of daytime and nighttime LSTs bymodel simulations compared with MODIS observations.

Generally, the spatial distribution of LSTs is well repro-duced in different seasons. The model simulations overes-timate both daytime and nighttime LSTs in the northwestmountain regions and underestimate daytime LSTs in theeast lower regions, especially in MAM, JJA, and SON.The uncertainty may be attributed to the homogeneouslapse rate of 6.5 K km−1 for air temperature interpolation.The linear calculation of V (Equation (13)) also affectsthe simulation of LSTs (Wang et al., 2011).

In general, the validation of spatially integrated andbasin-wide LST simulation has proved the WEB-DHMperforms well in representing energy budgets in this


Z. HU et al.

(a)

(c)

(b)

Figure 8. Monthly (a, b) and annual (c) time series of precipitation (P ), observed (Q _ obs) and simulated streamflow (Q _ sim) for the upperarea of the Huangfu gauge from 1985 to 2006. The precipitation is only given for reference in monthly time series (a, b).

basin. Because the model has been validated withmultiple-site streamflows in the Huangfuchuan RiverBasin, the additional validation of LSTs can give us moreconfidence in the model simulation of water and energycycles in this basin, especially in the impacted period.

4.3. Assessment of the causes of the decreasingstreamflow

4.3.1. Quantification of the impacts of climatefluctuations and human activities on streamflow

Taking the recorded streamflow prior to 1998 as a base-line, the contributions of climate fluctuations and humanactivities on the decreasing streamflow were quantita-tively assessed through Equations (8)–(11) (Table 3).Over the last decade of the study, the observed stream-flow has significantly decreased. The observed meanannual streamflow was 34.05 mm year−1 in 1985–1998,but only 13.72 mm year−1 in 1999–2006 which is 40%of the baseline value. The streamflow was reduced by10.38 mm year−1 as a result of climate fluctuations, whichwas estimated to be responsible for 51.03% of the totaldecrease. In addition, the human-induced decrease in thestreamflow was 9.96 mm year−1, which accounted for48.97% of the total reduction. Generally speaking, theclimate and human impacts on the decreasing streamflowover the past decade in Huangfuchuan River Basin arecomparable.

4.3.2. Analysis of climate- and human-induced factors

4.3.2.1. Climate-induced factors: Changes in the pre-cipitation and mean temperature and their correspondingeffects on ET were analysed. Figure 11 compares themean monthly water balance components in two sub-periods divided by the change-point. Influenced by thetemperate monsoon climate, 84% of the annual precip-itation is concentrated from May to September. Theseuneven precipitation characteristics are common in mostareas of Northern China. Considering the relatively hightemperatures that occur over the same period, relativelylarge ET occurs during these months, accounting for 77%of the annual total. Additionally, more than 90% of theprecipitation is lost to ET every year, which proves thatthe basin is representative of a typical semi-arid climate.

A comparison of the mean annual values between the1985–1998 and 1999–2006 periods (Table 3) shows thatthe precipitation decreased by 42.78 mm (12.55%) andthe mean temperature increased by 0.92 ◦C (10.32%).Meanwhile, the simulated ET decreased by 20.60 mm(6.64%), which was mainly attributed to the reducedprecipitation. Seasonally, the precipitation decreased by0.55 mm, 44.22 mm, and 1.9 mm and mean temperatureincreased by 1.26 ◦C, 1.05 ◦C, and 0.81 ◦C in MAM, JJA,and SON, respectively. Combined with the increased tem-perature, the precipitation decrease in summer inevitablyresulted in a reduction of runoff for this seasonalriver.



(a)

(b)

(c) (d)

Figure 9. Comparison of eight-daily LSTs between model simulations (LST _ WEB - DHM) and MODIS observations (LST _ MODIS) duringdaytime (a, c) and nighttime (b, d) averaged for the Huangfuchuan River Basin from March 2000 to December 2006. Time-series (a, b) and

scatter plots (c, d). Here, the missing data in MODIS LSTs and their corresponding simulated LSTs have been exempted for comparison.

Figure 12 displays the scattergram of monthly pre-cipitation and observed streamflow in the periods of1985–1998 and 1999–2006. The points in the latterperiod are lower than those in the previous period, whichimplies the streamflow generation ability is weakenedafter 1998. In general, the decreased precipitation andincreased temperature due to climate fluctuations haveplayed an important role in the streamflow reduction overthe past decade. Furthermore, the intensive effects ofhuman activities on the catchment’s water balance haveaggravated the situation.

4.3.2.2. Human-induced factors: The human-inducedchanges that contributed to the decreasing streamflowinclude soil and water conservation measures, artificialwater intake, and check dam and reservoir construction.Here, attention was paid mainly to the changes in humanactivities and their possible impacts on streamflow inrecent decade.

Indirect impacts . Table 4 displays the implementationof different soil and water conservation measures in1998, 2002, and 2006. According to this table, theamount of irrigated land remains stable, and the amountof terraced area shows a slight increase. However, theamounts of dammed land, forest land, and grasslandhave risen markedly since the end of the 1990s. By 2006,29.13 km2 of closed hillside area, 6.67 km2 of terraced

area, 17.05 km2 of dam field, 410.75 km2 of forest land,and 99.22 km2 of grassland have been constructed in thisbasin. Such large-scale implementation of different soiland water conservation measures not only changes themicro-topography but also has an important impact onthe land cover and soil features, which are closely relatedto the general characteristics of the catchment waterbalance (Yang et al., 2009; Xu et al., 2012). For instance,the increase in forest land and grassland can enhancecanopy interception and transpiration and weakenstreamflow generation, at least in short-term periods(Wang et al., 2010b). The terraced area can reduce thehillside slope and prolong the streamflow detention andthen increase soil water infiltration and reduce surfacerunoff.

Figure 13 shows the mean monthly components ofthe simulated ET for the subperiods of 1985–1998 and1999–2006. In WEB-DHM, the ET comes from thecanopy and soil surface, which both consist of two parts.The ET from the canopy includes canopy transpiration(E ct) and evaporation from canopy interception (E ci),whereas the bare soil evaporation comprises soil moistureloss from within the surface soil layer (E gs) and from soilsurface interception (E gi ). The comparison of the twosubperiods (see Table 3 and Figure 13) shows that the E ct

exhibits an increasing trend in most months, and the mean


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Simulated daytime LST

Observed daytime LST

Simulated nighttime LST

Observed nighttime LST

255

260

265

270

275

280

285

290

295

300

305

310

315

(d)

(c)

(b)

(a)

Figure 10. Seasonal changes (left to right: MAM, JJA, SON, and DJF) of the spatial distribution for daytime and nighttime LSTs (units: K) bymodel simulations (a, c) and MODIS observations (b, d) in the Huangfuchuan River Basin from 2000 to 2006. Here, the missing data in MODIS

LSTs and their corresponding simulated LSTs have been exempted for comparison.

annual value has increased by 18.66 mm. This could beattributed to the large-scale afforestation implemented inthis area, while the improvement in vegetation conditionsis reflected in the LAI variation. In particular, an increaseof 13.65 mm in E ct is obtained in JJA when the LAIobviously increases. In contrast, the mean annual E gs hasdecreased by 31.97 mm. This implies that because of thedecreased precipitation and increased vegetation extrac-tion, the soil surface moisture content has decreased

and the drier soil is not as conducive to streamflowgeneration.

Actually, the response of the hydrologic regime tounderlying surface changes is complicated, which invitesfurther observation, experiments, and investigation. Nev-ertheless, the soil conservation measures, as the humanactivities applied across the whole Loess Plateau, exert asignificant impact on water and sediment reduction (Jingand Zheng, 2004; Wang et al., 2008).

Table 3. Changes in the average annual precipitation (P ), mean temperature (T ), observed streamflow (Q _ obs), reconstructedstreamflow (Q _ sim), and simulated evapotranspiration (ET) and its components, such as canopy transpiration (E ct), evaporationfrom canopy interception (E ci), evaporation from surface soil layer (E gs), and evaporation from ground interception (E gi) during

the two subperiods.

Period P (mm) T (◦C) Q _ obs (mm) Q _ sim (mm) ET (mm) E ct (mm) E ci (mm) E gs (mm) E gi (mm)

1985–1998 340.93 8.92 34.05 34.03 310.11 40.62 4.02 174.10 91.371999–2006 298.15 9.84 13.72 23.68 289.51 59.29 3.75 142.13 84.35Change −42.78 0.92 −20.34 −10.35 −20.60 18.66 −0.28 −31.97 −7.02



(a)

(b)

Figure 11. Mean monthly observed streamflow (Q _ obs) and precipitation (P ), simulated streamflow (Q _ sim) and evapotranspiration (ET) forthe upper area of the Huangfu gauge during the periods of (a) 1985–1998 and (b) 1999–2006.

Direct impacts . The aforementioned human activitiescan have an indirect impact on the hydrologic cycle bychanging the underlying surface features. Other factors,such as artificial water intake and water storage projectconstruction, are regarded as having a direct impacton water resources. Figure 14 displays the amount ofartificial water intake in the years of 1998, 2002, and2006 in Jungar Banner, a major administrative districtof the Huangfuchuan River Basin (Figure 2(b)). Eco-nomic growth and population increases, along with thedecreased precipitation (which failed to meet the needsof the crops) and the increased temperatures (enhancingtranspiration) (Xu, 2008), caused agricultural water useand domestic water use to rise steadily from 1998 to2006, whereas the use of industrial water remained sta-ble. The total amount of water consumption has increasedby 17.61 million m3 (approximately 5.5 mm water depthfor this basin) over the 8-year period. Undoubtedly, theincreases in water intake have exacerbated the conditionsof water stress in this basin.

Through a field survey organized by the Hydrolog-ical Bureau of YRCC, 186 large-sized, 211 medium-sized, and 259 small-sized check dams were built by

Figure 12. Scatter diagram of monthly precipitation and observedstreamflow for the upper area of the Huangfu gauge in the periods

of 1985–1998 and 1999–2006.

the end of 2010, with a total storage capacity of 366.95million m3 (Table 5); this is larger than the total amountof water resources in the basin. These engineering mea-sures are widely implemented for trapping soil, clipping


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Table 4. Application of different soil and water conservation measures in the years 1998, 2002, and 2006 for the HuangfuchuanRiver Basin (unit: km2).

Year Terraced area Dammed land Irrigated land Forest land Grassland Hillsides closed forerosion control

Total

1998 20.99 17.66 13.02 513.75 113.83 0.02 679.282002 26.22 31.25 13.33 725.51 175.64 9.74 981.692006 27.66 34.71 13.55 924.51 213.05 29.13 1242.60

(b)

(a)

Figure 13. Mean monthly leaf area index (LAI), as well as mean monthly simulated components of evapotranspiration, including canopytranspiration (E ct), evaporation from canopy interception (E ci), evaporation from surface soil layer (E gs), and evaporation from ground interception

(E gi) for the upper area of the Huangfu gauge during the periods of (a) 1985–1998 and (b) 1999–2006.

peaks, and retaining floods on the Loess Plateau (Ranet al., 2008), but they also lead to the reduction of down-stream runoff to some extent (Jing and Zheng, 2004). Theretained water is mainly for economic activities, surfaceevaporation, and transpiration from crops planted in thedam farmland, whereas some is converted into ground-water. At present, the change of effective capacity yearby year, the lack of detailed siltation records, and the con-tinuing construction of check dams all make streamflowreduction calculations more difficult. In addition to thecheck dams, there are 18 small-sized reservoirs locatedin this basin, with a total storage capacity of 43.74 millionm3. As estimated by the Hydrology Bureau, the evapo-ration loss from these water conservancy works wouldexceed 10 million m3 per year (approximately 3.9 mm

water depth) (Table 5). Consequently, it can be inferredthat the impact of water storage projects on stream-flow decrease should not be ignored, which will furtherintensify the scarcity of water resources in the Huang-fuchuan River Basin.

4.4. Discussion of the simulation uncertainty

Although the model simulation has been verified onwater and energy cycles in different periods in this basin,uncertainty may still exist in the simulation processes.First, groundwater interactions between flow intervalsare not formulated in the WEB-DHM for simplicity andto reduce computation costs. Second, lateral moistureexchanges between model grids within a flow intervalare not considered. Such simplifications may lead to



Figure 14. Comparison of artificial water consumptions among different sections in the year of 1998, 2002, and 2006 in Jungar Banner(see Figure 2(b)), Ordos City, Inner Mongolia, China.

Table 5. Check dams in the Huangfuchuan River Basin.

Type Number Average time ofbuild-up (year)

Total watersurface area (km2)

Total storagecapacity (106 m3)

Total evaporationloss (106 m3/year)

Large-sized 186 1998 9.30 244.45 7.81Medium-sized 211 1993 4.22 108.56 3.54Small-sized 259 1979 1.30 13.95 1.09Total 656 – 14.82 366.95 12.44

some uncertainty, but they do not affect the model’sspatial structure to lump the topography and maintain ahigh efficiency for the simulation processes, especially inlarge-scale river basins (Wang et al., 2009a). In additionto the model structure, input data may be anothersource of uncertainty. Precipitation data were obtainedfrom 13 rainfall gauges, and other meteorological datahad a spatial and temporal resolution of 0.1◦ and 3-h,respectively, which might not be sufficient for modelsimulations because the model was executed with a 1-km grid size and hourly time steps. The mean annualwater balance error is −0.02 mm year−1 in the baselineperiod, which is much smaller than the value of thecontemporaneously observed mean annual streamflow(34.05 mm). But still, the simulation uncertainty requiresfurther investigation in future study.

As a first-order tributary of the middle reaches of theYellow River, the Huangfuchuan River Basin has beenthe subject of a few studies regarding the impact of cli-mate fluctuations on hydrologic regime. Xu et al. (2011)focus on quantifying the uncertainty of the impacts ofclimate change on river discharge associated with GCMstructure, emission scenarios, and prescribed increases inglobal mean temperature. Based on statistical analysis ofmeasured data, Wang et al. (2012) draw the conclusionthat the contribution rate of precipitation to the decreasedrunoff (base period 1960–1979) varies from 36.43% in1980–1997 to 16.81% in 1998–2008. Different fromprevious study, based on the numerical modelling andquantitative analysis, this study has drawn a conclusionthat the human impact on the decreasing streamflow is

comparable to that of climate fluctuations for this basinduring 1985–2006.

5. Conclusions

In this study, a distributed biosphere hydrological model(WEB-DHM) was applied to the semi-arid HuangfuchuanRiver Basin to simulate the natural hydrologic processover the period 1985–2006 with the aim of quantifyingthe effects of climate fluctuations and human activities onstreamflow change. The major findings from this studyare summarized below.

First, the MK test results demonstrated a decreas-ing trend in precipitation and an increasing trend inmean temperature; a warmer and drier climate exists inthe Huangfuchuan River Basin now compared with thebeginning of the study period. One major change-pointin 1998 for the annual streamflow series was identi-fied, so the study period was divided into two subperi-ods. The mean annual streamflow in the baseline period(1985–1998) was 34.05 mm year−1, whereas it was13.72 mm year−1 for the impacted period (1999–2006).This decrease of 20.34 mm year−1 is significant and ofcritical importance for typical semi-arid environments inwhich water crises are already severe.

Second, the calibrated WEB-DHM has the ability torepresent fine temporal-scale discharge processes withgood accuracy in the semi-arid Huangfuchuan RiverBasin over the baseline period. The daily simulateddischarges at Huangfu station agreed well with in situ


Z. HU et al.

observations in the calibration and verification periods,with RB values of −7.75% and 6.29%, respectively.

Finally, the WEB-DHM was used to separately quan-tify the contributions from climate- and human-inducedfactors to streamflow decrease by reconstructing the nat-ural streamflow in the impacted period (1999–2006).The results indicated that climate fluctuations and humanactivities accounted for a decrease of 10.38 mm (51.03%)and 9.96 mm (48.97%) in mean annual streamflow,respectively; both factors have comparable impacts onthe streamflow decrease in the Huangfuchuan RiverBasin. The distributed biosphere hydrological modellingapproach and the above findings will be beneficial forwater resource management in the semi-arid or arid riverbasins of China.

Acknowledgements

The study was funded by the National Natural Sci-ence Foundation of China (91125018 and 51009076),the International Science and Technology CooperationProgram of China (2010DFA21750) and the ChineseMinistry of Water Resources Program (200901019). Thesecond author (Dr. Lei Wang) was financially supportedby the Hundred Talents Program of Chinese Academy ofSciences. The authors are also grateful to two anonymousreviewers whose comments are helpful in improving thequality of this article.

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