Quantitative assessment of climate and human impacts on surface

INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. (2014)
Published online in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/joc.3965
Quantitative assessment of climate and human impacts
on surface water resources in a typical semi-arid watershed
in 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, China
b Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research,
Chinese Academy of Sciences, Beijing, China
c Department of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, USA
d
Hydrometeorology and Remote Sensing Laboratory, University of Oklahoma, Norman, OK, USA
e
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 of
the Yellow River have drastically decreased over the past decade, which has affected the governance strategies of the
entire Yellow River. The causes of the decrease in surface water are generally attributed to climate fluctuations and
human activities. In this study, a distributed biosphere hydrological model the Water and Energy Budget-based Distributed
Hydrological Model (WEB-DHM) and a Contribution Assessment method were jointly applied to diagnose and quantify
climate and human impacts on the streamflow change. Long-term hydrometeorological trends were analysed first and one
major change-point (in 1998) in the annual streamflow series was identified through the nonparametric Mann–Kendall
test and the annual precipitation-streamflow double cumulative curve method. The WEB-DHM model was calibrated and
validated over the baseline period of 1985–1998; the natural streamflow was reconstructed for the impacted period of
1999–2006. Then, the contributions of climate fluctuations and human activities to streamflow change were determined
quantitatively by comparing the natural streamflow with the observed value. The mean annual streamflow significantly
decreased from 34.05 mm year−1 to 13.72 mm year−1 in the baseline and impacted periods, respectively, showing a reduction
of 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-made
water storage infrastructure) caused a decrease of approximately 9.96 mm year−1 (48.97%). These findings are potentially
helpful 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 profound impact on various elements of the hydrologic cycle
(Ohmura and Wild, 2002; Barnett et al., 2008; Cong
et al., 2009). Specifically, the impact on surface water
resources (Wang and Hejazi, 2011; Zhang et al., 2012)
has attracted widespread attention and concern all over
the world. Studies indicate that the mean annual global
surface temperature has increased by 0.74 ◦ C in the past
100 years (1906–2005) (IPCC, 2007). Surface temperature increase leads to higher evaporation rates and enables
more water vapour transportation, therefore, accelerating the global hydrologic cycle (Menzel and B¨urger,
2002). Because of the redistribution of precipitation and
* Correspondence to: Z. Hu, State Key Laboratory of Hydro-science
and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China. E-mail: [email protected]
 2014 Royal Meteorological Society
the change in temperature, climate fluctuations have a
direct influence on the amount of water available (Middelkoop et al., 2001; Yang et al., 2004). Additionally,
human activities can also bring variability to the water
cycle and affect the spatial and temporal patterns of water
resources (Zhang et al., 2013). Cultivation, urbanization,
and other such human activities mainly affect basinand region-scale hydrologic processes via land use and
land cover change (DeFries and Eshleman, 2004; Zhang
and Schilling, 2006; Thanapakpawin et al., 2007; Hamdi
et al., 2011) and thus have an indirect effect on streamflow. In contrast, water resource withdrawal (Ma et al.,
2010) and return flow (Wang and Cai, 2010), hydraulic
construction and operation (Batalla et al., 2004), and
other anthropogenic modifications (Arrigoni et al., 2010)
have a direct impact on the availability of water resources
by altering their spatiotemporal distribution. In a broad
sense, human activities can also alter the hydrologic cycle
by disturbing correlated climate variables (Wang and
Z. HU et al.
Hejazi, 2011). However, from a practical point of view,
the human-induced impact on climate is not examined in
this study.
The Yellow River, also known as the Huanghe in
Chinese, is considered China’s mother river and the
cradle of Chinese civilization. It is an important water
source for hundreds of millions of people in the northern
and north-western parts of China. However, due to
climate fluctuations and increased human activities (Cong
et al., 2009), water scarcity has become more severe
in recent years; the drying up of the main river was
particularly aggravated in the 1990s (Zhang et al., 2009).
For example, in recent decade, the amount of surface
water resources in the Huangfuchuan River Basin, a semiarid watershed in the middle reaches of the Yellow River,
was less than a quarter of that observed in the First
National Water Resources Assessment (around the end of
the 1970s). Therefore, quantitative assessment of climate
and human impacts on surface water resources (e.g.
streamflow) is particularly important; it is the foundation
of regional water resources planning, management, and
sustainable development.
Traditionally, the contribution of human activities
to streamflow was estimated via the subentry investigation method (Wang et al., 2010b). However, an
increase in anthropogenic effects may introduce substantial uncertainty to the assessment results due to
problems involving restoration distortion and invalidation (Wang et al., 2003). On the basis of regression
methods, some researchers have tried to analyse the
sensitivity of streamflow to variations in precipitation,
evaporation, and other hydrometeorological variables to
assess the impact of climate fluctuations (Dooge et al.,
1999; Li et al., 2007; Jiang et al., 2011). Recently, as
remote sensing and geographic information system techniques have improved, many hydrologists have attempted
to quantify these impacts separately and execute water
resources assessments through hydrological modelling
method (Wang et al., 2010a; Bao et al., 2012; Zhang
et al., 2012). Despite some limitations to the application of hydrological modelling, such as the uncertainties arising from input data, model structure, and model
parameters, the data demanding and the trade-off between
simulation accuracy and computational cost (Xu and
Singh, 2004), hydrological modelling method is still good
approach in quantifying effects individually and to assess
the water resources (Wang et al., 2010b). In particular,
new-generation hydrological models with coupled water
and energy budgets are more promising in applications
involving relatively dry conditions (e.g. arid or semi-arid
river basins; see Wang et al., 2011), when compared with
traditional water-balance models.
The objective of this study is to quantitatively analyse
the impacts of climate fluctuations and human activities
on the decreasing streamflow in the semi-arid Huangfuchuan River Basin in the middle reaches of the Yellow
River from 1985 to 2006. We analyse the hydrometeorological trends and change-points and then simulate the natural hydrologic processes for a period of 22
 2014 Royal Meteorological Society
consecutive years, using a calibrated biosphere hydrological model. On the basis of observations and hydrological simulations, the contributions of climate fluctuations and human activities to the streamflow changes
are quantitatively determined. This study is potentially
valuable and practical for improving the understanding
of the hydrologic cycle and promoting water resources
planning and management in the middle reaches of the
Yellow 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 and
identify change-points of precipitation, mean temperature, and streamflow in the Huangfuchuan River Basin.
This methodology can handle non-normalities with high
asymptotic efficiency (Berryman et al., 1988), and it is
widely used for the analysis of trends in various hydrometeorological series (Zhang et al., 2009; Jiang et al.,
2011). For a time series X = {x 1 ,x 2 , . . . ,x n } (n > 10), the
MK test statistic Z is calculated as follows (Xu et al.,
2003):
 S −1

 √var(S ) , S > 0
Z =
in which
S =
0
S =0

 √S +1 , S < 0
var(S )
n−1 n
,
(1)
sgn (xk − xi ),
(2)
i =1 k =i +1
where sgn(θ) is equal to 1, 0, or −1 when θ is
greater than, equal to, or less than 0, respectively. The
null hypothesis, H0 , stands that there is no statistically
significant 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 the
standard normal distribution for a given significance level
α. Otherwise, the H1 hypothesis is accepted, and the trend
is statistically significant. A positive Z value denotes
an increasing trend, and the opposite demonstrates a
decreasing trend.
To determine the occurrence of a change-point in a data
series, the test statistic (UFi ) is estimated by the flowing
formulas (Zhang et al., 2007; Bao et al., 2012):
Si − E (Si )
UFi = √
var (Si )
Sk =
ri =
k
ri
(i = 1, 2, . . . , n) ,
(k = 2, 3, . . . , n) ,
(3)
(4)
i =1
1, xi > xj
0, xi ≤ xj
(j = 1, 2, . . . , i − 1) .
(5)
Because x i is an independent and identically distributed random variable, the expected value E (S i ) and
Int. J. Climatol. (2014)
LONG-TERM WATER RESOURCES IN MIDDLE YELLOW RIVER
2.3.
variance var(S i ) can be given as follows:
E (Si ) =
i (i − 1)
,
4
(6)
i (i − 1) (2i + 5)
.
(7)
72
Then, by the calculation of UFi for the inverse
time series x n , x n − 1 , . . . , x 1 again and the definition of
UBi = − UF i , i = n, n − 1, . . . , 1, the curve of UFi and
UBi can be plotted. If a match point of the two curves
exists and the series trend is statistically significant, the
match point can be regarded as a change-point of the
series with high probability.
var (Si ) =
2.2. Contribution assessment of the climate and human
impacts on surface water resources
For the purpose of separating and quantifying the climateand human-induced impacts on streamflow variation,
the hydrological model simulation method was adopted
in this study, along with the hypothesis that climate
fluctuations and human activities are independent (Wang
et al., 2008). Through trend and change-point analysis,
the whole streamflow series can be divided into a baseline
period series and an impacted period series. The observed
streamflow change between the two periods demonstrates
the combined influences of climate fluctuations and
increased human activities (Bao et al., 2012), which can
be expressed as:
QT = QC + QH = QOI − QB ,
(8)
where the total change of the annual streamflow (Q T )
includes two parts: the streamflow change caused by
climate fluctuations (Q C ) and human activities (Q H ),
whereas Q B and Q OI are the observed mean annual
streamflow in the baseline period and the impacted
period, respectively.
Later, a hydrological model is calibrated in the baseline period and then forced by meteorological data to
simulate natural streamflow in the impacted period (Q SI ),
without consideration of local human activities; this can
therefore be regarded as the hydrologic response to climate variation only (Wang et al., 2010a). In this way,
the difference between the reconstructed mean annual
streamflow and baseline value which is the observed
mean annual streamflow in the baseline period can be
considered as representing the influence of climate fluctuations on streamflow change (Wang et al., 2008).
QC = QSI − QB
(9)
Finally, the contribution of climate fluctuations and
human activities to streamflow change, which are defined
as ηC and ηH , respectively, are quantitatively estimated
by:
QC
× 100%,
(10)
ηC =
QT
ηH =
QH
× 100%.
QT
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(11)
WEB-DHM model
As a distributed biosphere hydrological model, WEBDHM (the Water and Energy Budget-based Distributed
Hydrological Model; Wang et al., 2009a, 2009b, 2009c)
is developed by fully coupling a simple biosphere
scheme SiB2 (Sellers et al., 1996a) with a hillslope-based
hydrological model, Geomorphology-Based Hydrological
Model (GBHM) (Yang et al., 2002). It has the ability to
consistently describe water, energy, and CO2 fluxes in a
basin. The model has been applied to simulate discharge,
fluxes, land surface temperature (LST), and surface soil
moisture in multiple time and space scales in several river
basins (Wang et al , 2009b; Jaranilla-Sanchez et al., 2011)
with reliable accuracies, including in semi-arid environments (Wang et al , 2011). As illustrated in Figure 1, the
overall structure of WEB-DHM can be described as follows:
(1) A digital elevation model (DEM) is used to define the
research basin and then it is divided into sub-basins
(see Figure 1(a)). With regard to each sub-basin, flow
intervals are specified to represent time lags and the
accumulating processes in the river network. Each
flow interval is composed of several model grids (see
Figure 1(b)).
(2) For each model grid, there is one combination of
land use type and soil type. Here, the land surface
submodel is used to independently calculate the
transfer of turbulent fluxes between the atmosphere
and land surface (see Figure 1(b) and (d)). The
vertical water distributions for all the model grids
can be obtained through this biosphere process.
(3) Each model grid is subdivided into a number of geometrically symmetrical hillslopes (see Figure 1(c)).
In WEB-DHM, a hillslope with unit length is named
a Basic Hydrological Unit (BHU). Within a given
BHU, the hydrological submodel is used to simulate lateral water redistribution and to calculate
runoff consisting of overland, lateral subsurface, and
groundwater flows (see Figure 1(c) and (d)). The total
response of all BHUs within a model grid comprises
the runoff for a grid cell.
(4) Simplifications have been made in which streams
located in one flow interval are lumped into a
single virtual channel. All of the flow intervals are
connected by the river network generated from the
DEM. All runoff from the grid cells in the given flow
interval accumulates in the virtual channel directed
toward the outlet of the river basin. The flow routing
for the entire river network in the basin is simulated
using the kinematic wave approach.
In this study, the LST can be estimated by the WEBDHM following Wang et al. (2009b).
1/4
,
(12)
Tsim = V × Tc4 + (1 − V ) × Tg4
V = LAI/LAImax ,
(13)
Int. J. Climatol. (2014)
Z. HU et al.
(a)
(b)
Flow
Intervals
9
2
4
Subbasin
8
7
3
5
6
1
Outlet
(c)
(d)
Grid size in the model
Precipitation
R lw
R sw
H
λET
CO2
Soil surface
Surface flow
Inter flow
Groundwater table
River
Groundwater flow
Impervious Surface
Datum
l
DEM grid size
Hillslope Unit
Figure 1. Overall structure of WEB-DHM model: (a) division from a basin to sub-basins; (b) subdivision from a sub-basin to flow intervals
comprising several model grids; (c) discretization from a model grid to a number of geometrically symmetrical hillslopes, and (d) process
descriptions of the water moisture transfer from atmosphere to river. Here, R sw and R lw 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 vegetation
coverage; T c and T g are the temperature of the canopy
and the soil surface, respectively; LAI is the leaf area
index and LAImax is the maximum LAI values derived
following Sellers et al. (1996b).
3.
3.1.
Datasets
Study region
Huangfuchuan River Basin originates in Southern Inner
Mongolia and encompasses the area from the southeastern part of Erdos Plateau to the northern edge of
Loess Plateau. It covers longitudes from 110.33◦ E to
111.25◦ E and latitudes from 39.20◦ N to 39.99◦ N (see
Figure 2(b)), with a catchment area of 3186 km2 . Characterized by a semi-arid continental climate, the basin’s
average precipitation and mean temperature from 1961
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to 2000 were 388 mm and 7.5 ◦ C, respectively (Xu et al.,
2011). The annual precipitation shows high temporal
variability; 76% falls between June and September, and
53% falls in July and August. Consequently, the temporal
distribution of runoff is uneven, and runoff that occurs in
the flood season (June to September) accounts for 82.6%
of the annual amounts. Moreover, as a seasonal river, the
flow characteristics are representative and typical: high
peak discharge, short flood duration, and rapidly rising
and falling flood speeds.
3.2. Available data
The input datasets for the Huangfuchuan River Basin
used in WEB-DHM are described below.
Precipitation data recorded at 13 rainfall gauges from
1985 to 2006 were provided by the Hydrological Bureau
of Yellow River Conservancy Commission (YRCC).
Hourly precipitation data were only available in the flood
Int. J. Climatol. (2014)
LONG-TERM WATER RESOURCES IN MIDDLE YELLOW RIVER
(a)
100°0’0"E
105°0’0"E
110°0’0"E
115°0’0"E
120°0’0"E
Hekouzhen
Basin
Yellow River
Discharge Gauge
LongmenHejin
Huaxian
Huayuankou
0 150 300
(b)
100°0’0"E
105°0’0"E
110°0’0"E
115°0’0"E
600 Km
120°0’0"E
40°0’0"N
111°0’0"E
40°0’0"N
110°30’0"E
35°0’0"N
Lijin
LongyangxiaLanzhou
95°0’0"E
40°0’0"N
Legend
35°0’0"N
40°0’0"N
95°0’0"E
39°30’0"N
39°30’0"N
Shagedu
39°0’0"N
Basin Boundary
Jungar Banner
Fugu county
River
Discharge Gauge
Rainfall Gauge
110°30’0"E
0 5 10
20 Km
39°0’0"N
Huangfu
Legend
111°0’0"E
Figure 2. The Huangfuchuan River Basin: (a) location within the
Yellow River Basin and (b) the locations of hydrometeorological
stations in the basin.
season. In the non-flood season, daily observations were
available and were downscaled to hourly data using the
precipitation duration (Wang et al., 2010b).
Meteorological data were extracted from the China
Meteorological Forcing Dataset (http://westdc.westgis.
ac.cn/data/7a35329c-c53f-4267-aa07-e0037d913a21; see
He, 2010; Yang et al., 2010; Chen et al., 2011). This
dataset includes air temperature, air pressure, specific
humidity, wind speed, precipitation rate, and downward
shortwave and longwave radiation, with a spatial and
temporal resolution of 0.1◦ and 3-h, respectively. These
meteorological variables (except precipitation rate)
were linearly interpolated to model grids (1000 m) for
simulations.
Geographical information used for the WEB-DHM
mainly included topography, land use, soil type and
vegetation (Figure 3). Digital elevation data used were
the NASA STRM (http://eros.usgs.gov/#/Find_Data/
Products_and_Data_Available/SRTM), using approximately 90-m resolution data resampled to a 1000-m
DEM. The subgrid topography was described by a 25-m
DEM, which was generated from 30-m ASTER GDEM
(http://www.gdem.aster.ersdac.or.jp/index.jsp) and was
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used to calculate the topographic parameters (hillslope
length and angle).
Land use types were reclassified to three SiB2 categories, which were provided by Environmental & Ecological Science Data Center for West China, National
Natural Science Foundation of China (http://westdc.
westgis.ac.cn).
Soil type and hydraulic characteristics, including saturated soil-moisture content, residual soil-moisture content, and saturated hydrological conductivity for soil
surface and van Genuchten parameters (α and n) (van
Genuchten, 1980), were obtained from the Food and
Agriculture Organization (FAO) global dataset (FAO,
2003), with a 5-arc minute spatial resolution.
The vegetation static parameters, such as morphological, optical, and physiological properties, were defined
following Sellers et al. (1996b). The dynamic vegetation
parameters are Leaf Area Index (LAI) and Fraction of
Photosynthetically Active Radiation (FPAR) absorbed by
the green vegetation canopy. In this study, they were
obtained from the NOAA AVHRR PAL 16-km satellite
dataset (Myneni et al., 1997) for the period 1985 to 2000
and 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 satellite
observation data were used to evaluate the WEB-DHM
performance in simulating water and energy budgets
(Wang et al., 2009b). Daily discharge data at two discharge gauges (Shagedu and Huangfu; see Figure 2) was
obtained from the Hydrological Bureau of YRCC. The
LSTs were obtained from the NASA MODIS MOD11A2
V5 1-km 8-day product (Wan, 2008), which has been
available since 5 March 2000. A set of independent statistical datasets from the Hydrological Bureau of YRCC
and the Upper and Middle Yellow River Bureau were
collected and compiled to analyse the impact of human
activities.
4.
Results and discussion
4.1. Trends and change-point analysis for
hydro-meteorological variables
A study period from 1985 to 2006 was selected, considering the data availability and recorded streamflow
changes. Figure 4 plots the annual time series of precipitation, mean temperature, observed streamflow, and their
long-term linear trends. Both precipitation and observed
streamflow show a decreasing trend, whereas the mean
temperature shows a remarkable increasing trend. Moreover, the precipitation and observed streamflow decreased
by 0.43% and 3.85% per year, respectively. The climate of the basin became warmer and drier over the
study period. Besides, the MK test results (Table 1)
show the same trends as mentioned above. Specifically,
the decreasing trend for observed streamflow and the
increasing trend for mean temperature are statistically
significant.
Int. J. Climatol. (2014)
Z. HU et al.
(a)
(b)
Legend
Legend
Basin Boundary
Dem
1468
Basin Boundary
Slope(degree)
14.20
1.40
848
0
5
10
0
20 Km
(c)
5
10
20 Km
(d)
Legend
Legend
Basin Boundary
Land Use Type
Broadleaf-Deciduous Trees
Shrubs with Bare Soil
Agriculture/C3 Grassland
0
5
10
Basin Boundary
Soil Type
CAMBISOLS
KASTANOZEMS
20 Km
0
5
10
20 Km
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 precipitation, mean temperature, and streamflow time series.
MK test (α = 0.1)
Factor
Precipitation
Mean temperature
Streamflow
Z
H0
Chang-point analysis
−0.11
3.13
−1.69
A
R
R
–
1996
1998
R: reject H0 ; A: accept H0 .
To investigate intra-annual variability, Figure 5
presents the MK test results for seasonal hydrometeorological variables. Among them, the precipitation
trend varies seasonally, with a decreasing trend in JJA
and 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
 2014 Royal Meteorological Society
trend for mean temperature and a decreasing trend for
observed streamflow in all seasons, which means a
greater extent of warmer and drier conditions throughout
the basin. The trends for evapotranspiration (ET),
which is the difference between the precipitation and
contemporaneous streamflow, are consistent with the
precipitation. This implies that the water supply is the
limiting factor for ET other than the energy supply in
this basin.
Figure 6(a) demonstrates that one change-point is
detected in 1998 for the annual observed streamflow
series through the MK change-point test. Moreover,
the annual precipitation-streamflow double cumulative
curve (Figure 6(b)) used as auxiliary material for
change-point detection shows that the relationship
between precipitation and streamflow has changed
since 1998. Because the results from both methods are
Int. J. Climatol. (2014)
LONG-TERM WATER RESOURCES IN MIDDLE YELLOW RIVER
(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 ), observed
streamflow (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 with
relatively high probability, and a baseline period of
1985–1998 and an impacted period of 1999–2006 are
distinguished 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 daily
discharge at Huangfu station from 1985 to 1990. Several parameters were optimized through trial and error
methods by matching the simulated and observed daily
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discharge at Huangfu station. The basin-averaged parameters are described in Table 2.
To display the calibration results more clearly,
Figure 7(a) illustrates the anomaly curves of the
observed and simulated daily discharge at Huangfu.
The average daily hydrograph is calculated based on
the observed value from 1985 to 1990. It is shown that
the WEB-DHM can reconstruct the fine temporal-scale
discharge processes (reproducing both the peak and
base flows) well, with a Nash-Sutcliffe coefficient of
efficiency (NSCE; Nash and Sutcliffe, 1970) equal to
0.913 and a relative bias (RB; Wang et al., 2010b)
equal to −7.75%. In addition, the simulated discharge
Int. J. Climatol. (2014)
Z. HU et al.
(a)
(b)
Figure 6. (a) Mann-Kendall’s testing statistic values (UFi ) and (b) precipitation-streamflow double cumulative curve for change-point analysis
of annual observed streamflow in Huangfuchuan River Basin (1985–2006).
Table 2. Basin-averaged values of the parameters used in the Huangfuchuan River Basin.
Symbol
θs
θr
α
n
anik
Ks
Dr
Parameters
Saturated volumetric moisture content
of unsaturated zone
Residual volumetric moisture content
of unsaturated zone
van Genuchten parameter
van Genuchten parameter
Hydraulic conductivity anisotropy
ratio
Saturated hydraulic conductivity for
soil surface
Root depth (D 1 + D 2 )
Unit
Basin-averaged value
Source
−3
0.45
FAO (2003)
m3 m−3
0.07
FAO 2003)
0.01
1.60
32.10
FAO (2003)
FAO (2003)
Optimization
mm h−1
3.77
Optimization
m
1.02
Sellers et al. (1996b)
3
m m
at Shagedu (Figure 7(b)) also agrees well with observed
values, with NSCE of 0.927 and RB of −3.20%.
Using the same parameter values, the model was
then validated from 1991 to 1998. The daily anomaly
hydrographs at Huangfu and Shagedu (Figure 7(c) and
(d)) show acceptable accuracy; the NSCE is equal
to 0.675 and 0.706 and the RB is equal to 6.29%
and −13.52%, respectively. These results confirm the
generally good performance of the WEB-DHM in daily
streamflow simulation.
Figure 8(a) is the time series of monthly observed
(Q _ obs) and simulated discharge (Q _ sim) for the upper
area of the Huangfu gauge from 1985 to 1998, with
the precipitation (P ) time series given for reference.
Despite some differences at the peaks, there is fairly
good agreement between the simulated and observed
streamflow, with NSCE and RB values of 0.936 and
−0.07%, respectively.
Generally, the calibration and verification results
demonstrate that the WEB-DHM can simulate daily and
monthly natural streamflow with good accuracy in the
Huangfuchuan River Basin. The outputs of the calibrated
WEB-DHM are reliable, and the model can be applied
 2014 Royal Meteorological Society
to simulate natural streamflow series during the impacted
period (1999–2006).
4.2.2. Natural streamflow reconstruction for the
impacted period
Without considering local human activities (e.g. landuse change), the benchmarked model was forced to
reconstruct natural streamflow through the baseline and
impacted period. Figure 8 depicts the time series of
monthly and annual observed and reconstructed streamflow for the whole period. The verification period with
the decreased model performance may indicate intensified
human impacts, and the significant differences between
simulations and observations since the beginning of the
impacted period confirm this viewpoint (Wang et al.,
2008).
Figure 9 gives the comparison of 8-daily LSTs
between the WEB-DHM simulations (LST _ WEB DHM) and MODIS observations (LST _ MODIS) at
daytime (around 10:30 hours at local time) and nighttime
(around 22:30 hours at local time) averaged for the basin
from March 2000 to December 2006. The results show
that the simulated LSTs agree well with the MODIS
Int. J. Climatol. (2014)
LONG-TERM WATER RESOURCES IN MIDDLE YELLOW RIVER
(a)
(b)
(c)
(d)
Figure 7. Observed and simulated daily discharge anomaly curves in the calibration (1985–1990) and verification (1991–1998) periods at the
Huangfu (a, c) and Shagedu (b, d) gauges.
LSTs except that some simulations are slightly overestimated. The mean bias error (MBE; Wang et al., 2011)
is equal to 0.19 K and 1.49 K and the root mean square
error (RMSE) is equal to 2.83 K and 2.47 K during the
day and at night, respectively (Figure 9(a) and (b)). The
scatter plots also show the good consistency between
the simulations and observations for both daytime and
nighttime LSTs, with the correlation coefficient (R) being
0.9826 and 0.9875, respectively (Figure 9(c) and (d)).
Figure 10 describes the seasonal changes of the
spatial distribution of daytime and nighttime LSTs by
model simulations compared with MODIS observations.
 2014 Royal Meteorological Society
Generally, the spatial distribution of LSTs is well reproduced in different seasons. The model simulations overestimate both daytime and nighttime LSTs in the northwest
mountain regions and underestimate daytime LSTs in the
east lower regions, especially in MAM, JJA, and SON.
The uncertainty may be attributed to the homogeneous
lapse rate of 6.5 K km−1 for air temperature interpolation.
The linear calculation of V (Equation (13)) also affects
the simulation of LSTs (Wang et al., 2011).
In general, the validation of spatially integrated and
basin-wide LST simulation has proved the WEB-DHM
performs well in representing energy budgets in this
Int. J. Climatol. (2014)
Z. HU et al.
(a)
(b)
(c)
Figure 8. Monthly (a, b) and annual (c) time series of precipitation (P ), observed (Q _ obs) and simulated streamflow (Q _ sim) for the upper
area 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 with
multiple-site streamflows in the Huangfuchuan River
Basin, the additional validation of LSTs can give us more
confidence in the model simulation of water and energy
cycles in this basin, especially in the impacted period.
4.3. Assessment of the causes of the decreasing
streamflow
4.3.1. Quantification of the impacts of climate
fluctuations and human activities on streamflow
Taking the recorded streamflow prior to 1998 as a baseline, the contributions of climate fluctuations and human
activities on the decreasing streamflow were quantitatively assessed through Equations (8)–(11) (Table 3).
Over the last decade of the study, the observed streamflow has significantly decreased. The observed mean
annual 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 by
10.38 mm year−1 as a result of climate fluctuations, which
was estimated to be responsible for 51.03% of the total
decrease. In addition, the human-induced decrease in the
streamflow was 9.96 mm year−1 , which accounted for
48.97% of the total reduction. Generally speaking, the
climate and human impacts on the decreasing streamflow
over the past decade in Huangfuchuan River Basin are
comparable.
 2014 Royal Meteorological Society
4.3.2. Analysis of climate- and human-induced factors
4.3.2.1. Climate-induced factors: Changes in the precipitation and mean temperature and their corresponding
effects on ET were analysed. Figure 11 compares the
mean monthly water balance components in two subperiods divided by the change-point. Influenced by the
temperate monsoon climate, 84% of the annual precipitation is concentrated from May to September. These
uneven precipitation characteristics are common in most
areas of Northern China. Considering the relatively high
temperatures that occur over the same period, relatively
large ET occurs during these months, accounting for 77%
of the annual total. Additionally, more than 90% of the
precipitation is lost to ET every year, which proves that
the basin is representative of a typical semi-arid climate.
A comparison of the mean annual values between the
1985–1998 and 1999–2006 periods (Table 3) shows that
the precipitation decreased by 42.78 mm (12.55%) and
the 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 reduced
precipitation. Seasonally, the precipitation decreased by
0.55 mm, 44.22 mm, and 1.9 mm and mean temperature
increased by 1.26 ◦ C, 1.05 ◦ C, and 0.81 ◦ C in MAM, JJA,
and SON, respectively. Combined with the increased temperature, the precipitation decrease in summer inevitably
resulted in a reduction of runoff for this seasonal
river.
Int. J. Climatol. (2014)
LONG-TERM WATER RESOURCES IN MIDDLE YELLOW RIVER
(a)
(b)
(c)
(d)
Figure 9. Comparison of eight-daily LSTs between model simulations (LST _ WEB - DHM) and MODIS observations (LST _ MODIS) during
daytime (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 precipitation and observed streamflow in the periods of
1985–1998 and 1999–2006. The points in the latter
period are lower than those in the previous period, which
implies the streamflow generation ability is weakened
after 1998. In general, the decreased precipitation and
increased temperature due to climate fluctuations have
played an important role in the streamflow reduction over
the past decade. Furthermore, the intensive effects of
human activities on the catchment’s water balance have
aggravated the situation.
4.3.2.2. Human-induced factors: The human-induced
changes that contributed to the decreasing streamflow
include soil and water conservation measures, artificial
water intake, and check dam and reservoir construction.
Here, attention was paid mainly to the changes in human
activities and their possible impacts on streamflow in
recent decade.
Indirect impacts. Table 4 displays the implementation
of different soil and water conservation measures in
1998, 2002, and 2006. According to this table, the
amount of irrigated land remains stable, and the amount
of terraced area shows a slight increase. However, the
amounts of dammed land, forest land, and grassland
have risen markedly since the end of the 1990s. By 2006,
29.13 km2 of closed hillside area, 6.67 km2 of terraced
 2014 Royal Meteorological Society
area, 17.05 km2 of dam field, 410.75 km2 of forest land,
and 99.22 km2 of grassland have been constructed in this
basin. Such large-scale implementation of different soil
and water conservation measures not only changes the
micro-topography but also has an important impact on
the land cover and soil features, which are closely related
to the general characteristics of the catchment water
balance (Yang et al., 2009; Xu et al., 2012). For instance,
the increase in forest land and grassland can enhance
canopy interception and transpiration and weaken
streamflow generation, at least in short-term periods
(Wang et al., 2010b). The terraced area can reduce the
hillside slope and prolong the streamflow detention and
then increase soil water infiltration and reduce surface
runoff.
Figure 13 shows the mean monthly components of
the simulated ET for the subperiods of 1985–1998 and
1999–2006. In WEB-DHM, the ET comes from the
canopy 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 moisture
loss from within the surface soil layer (E gs ) and from soil
surface interception (E gi ). The comparison of the two
subperiods (see Table 3 and Figure 13) shows that the E ct
exhibits an increasing trend in most months, and the mean
Int. J. Climatol. (2014)
Z. HU et al.
(a)
Simulated daytime LST
315
310
(b)
Observed daytime LST
305
300
295
290
(c)
Simulated nighttime LST
285
280
275
270
(d)
Observed nighttime LST
265
260
255
Figure 10. Seasonal changes (left to right: MAM, JJA, SON, and DJF) of the spatial distribution for daytime and nighttime LSTs (units: K) by
model 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 be
attributed to the large-scale afforestation implemented in
this area, while the improvement in vegetation conditions
is reflected in the LAI variation. In particular, an increase
of 13.65 mm in E ct is obtained in JJA when the LAI
obviously increases. In contrast, the mean annual E gs has
decreased by 31.97 mm. This implies that because of the
decreased precipitation and increased vegetation extraction, the soil surface moisture content has decreased
and the drier soil is not as conducive to streamflow
generation.
Actually, the response of the hydrologic regime to
underlying surface changes is complicated, which invites
further observation, experiments, and investigation. Nevertheless, the soil conservation measures, as the human
activities applied across the whole Loess Plateau, exert a
significant impact on water and sediment reduction (Jing
and Zheng, 2004; Wang et al., 2008).
Table 3. Changes in the average annual precipitation (P ), mean temperature (T ), observed streamflow (Q _ obs), reconstructed
streamflow (Q _ sim), and simulated evapotranspiration (ET) and its components, such as canopy transpiration (E ct ), evaporation
from 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
1999–2006
Change
340.93
298.15
−42.78
8.92
9.84
0.92
34.05
13.72
−20.34
34.03
23.68
−10.35
310.11
289.51
−20.60
40.62
59.29
18.66
4.02
3.75
−0.28
174.10
142.13
−31.97
91.37
84.35
−7.02
 2014 Royal Meteorological Society
Int. J. Climatol. (2014)
LONG-TERM WATER RESOURCES IN MIDDLE YELLOW RIVER
(a)
(b)
Figure 11. Mean monthly observed streamflow (Q _ obs) and precipitation (P ), simulated streamflow (Q _ sim) and evapotranspiration (ET) for
the upper area of the Huangfu gauge during the periods of (a) 1985–1998 and (b) 1999–2006.
Direct impacts. The aforementioned human activities
can have an indirect impact on the hydrologic cycle by
changing the underlying surface features. Other factors,
such as artificial water intake and water storage project
construction, are regarded as having a direct impact
on water resources. Figure 14 displays the amount of
artificial water intake in the years of 1998, 2002, and
2006 in Jungar Banner, a major administrative district
of the Huangfuchuan River Basin (Figure 2(b)). Economic growth and population increases, along with the
decreased precipitation (which failed to meet the needs
of the crops) and the increased temperatures (enhancing
transpiration) (Xu, 2008), caused agricultural water use
and domestic water use to rise steadily from 1998 to
2006, whereas the use of industrial water remained stable. The total amount of water consumption has increased
by 17.61 million m3 (approximately 5.5 mm water depth
for this basin) over the 8-year period. Undoubtedly, the
increases in water intake have exacerbated the conditions
of water stress in this basin.
Through a field survey organized by the Hydrological Bureau of YRCC, 186 large-sized, 211 mediumsized, and 259 small-sized check dams were built by
 2014 Royal Meteorological Society
Figure 12. Scatter diagram of monthly precipitation and observed
streamflow 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 366.95
million m3 (Table 5); this is larger than the total amount
of water resources in the basin. These engineering measures are widely implemented for trapping soil, clipping
Int. J. Climatol. (2014)
Z. HU et al.
Table 4. Application of different soil and water conservation measures in the years 1998, 2002, and 2006 for the Huangfuchuan
River Basin (unit: km2 ).
Year
Terraced area
Dammed land
Irrigated land
Forest land
Grassland
Hillsides closed for
erosion control
Total
1998
2002
2006
20.99
26.22
27.66
17.66
31.25
34.71
13.02
13.33
13.55
513.75
725.51
924.51
113.83
175.64
213.05
0.02
9.74
29.13
679.28
981.69
1242.60
(a)
(b)
Figure 13. Mean monthly leaf area index (LAI), as well as mean monthly simulated components of evapotranspiration, including canopy
transpiration (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 (Ran
et al., 2008), but they also lead to the reduction of downstream runoff to some extent (Jing and Zheng, 2004). The
retained water is mainly for economic activities, surface
evaporation, and transpiration from crops planted in the
dam farmland, whereas some is converted into groundwater. At present, the change of effective capacity year
by year, the lack of detailed siltation records, and the continuing construction of check dams all make streamflow
reduction calculations more difficult. In addition to the
check dams, there are 18 small-sized reservoirs located
in this basin, with a total storage capacity of 43.74 million
m3 . As estimated by the Hydrology Bureau, the evaporation loss from these water conservancy works would
exceed 10 million m3 per year (approximately 3.9 mm
 2014 Royal Meteorological Society
water depth) (Table 5). Consequently, it can be inferred
that the impact of water storage projects on streamflow decrease should not be ignored, which will further
intensify the scarcity of water resources in the Huangfuchuan River Basin.
4.4. Discussion of the simulation uncertainty
Although the model simulation has been verified on
water and energy cycles in different periods in this basin,
uncertainty may still exist in the simulation processes.
First, groundwater interactions between flow intervals
are not formulated in the WEB-DHM for simplicity and
to reduce computation costs. Second, lateral moisture
exchanges between model grids within a flow interval
are not considered. Such simplifications may lead to
Int. J. Climatol. (2014)
LONG-TERM WATER RESOURCES IN MIDDLE YELLOW RIVER
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
Large-sized
Medium-sized
Small-sized
Total
Number
Average time of
build-up (year)
Total water
surface area (km2 )
Total storage
capacity (106 m3 )
Total evaporation
loss (106 m3 /year)
186
211
259
656
1998
1993
1979
–
9.30
4.22
1.30
14.82
244.45
108.56
13.95
366.95
7.81
3.54
1.09
12.44
some uncertainty, but they do not affect the model’s
spatial structure to lump the topography and maintain a
high efficiency for the simulation processes, especially in
large-scale river basins (Wang et al., 2009a). In addition
to the model structure, input data may be another
source of uncertainty. Precipitation data were obtained
from 13 rainfall gauges, and other meteorological data
had a spatial and temporal resolution of 0.1◦ and 3-h,
respectively, which might not be sufficient for model
simulations because the model was executed with a 1km grid size and hourly time steps. The mean annual
water balance error is −0.02 mm year−1 in the baseline
period, which is much smaller than the value of the
contemporaneously observed mean annual streamflow
(34.05 mm). But still, the simulation uncertainty requires
further investigation in future study.
As a first-order tributary of the middle reaches of the
Yellow River, the Huangfuchuan River Basin has been
the subject of a few studies regarding the impact of climate fluctuations on hydrologic regime. Xu et al. (2011)
focus on quantifying the uncertainty of the impacts of
climate change on river discharge associated with GCM
structure, emission scenarios, and prescribed increases in
global mean temperature. Based on statistical analysis of
measured data, Wang et al. (2012) draw the conclusion
that the contribution rate of precipitation to the decreased
runoff (base period 1960–1979) varies from 36.43% in
1980–1997 to 16.81% in 1998–2008. Different from
previous study, based on the numerical modelling and
quantitative analysis, this study has drawn a conclusion
that the human impact on the decreasing streamflow is
 2014 Royal Meteorological Society
comparable to that of climate fluctuations for this basin
during 1985–2006.
5.
Conclusions
In this study, a distributed biosphere hydrological model
(WEB-DHM) was applied to the semi-arid Huangfuchuan
River Basin to simulate the natural hydrologic process
over the period 1985–2006 with the aim of quantifying
the effects of climate fluctuations and human activities on
streamflow change. The major findings from this study
are summarized below.
First, the MK test results demonstrated a decreasing trend in precipitation and an increasing trend in
mean temperature; a warmer and drier climate exists in
the Huangfuchuan River Basin now compared with the
beginning of the study period. One major change-point
in 1998 for the annual streamflow series was identified, so the study period was divided into two subperiods. The mean annual streamflow in the baseline period
(1985–1998) was 34.05 mm year−1 , whereas it was
13.72 mm year−1 for the impacted period (1999–2006).
This decrease of 20.34 mm year−1 is significant and of
critical importance for typical semi-arid environments in
which water crises are already severe.
Second, the calibrated WEB-DHM has the ability to
represent fine temporal-scale discharge processes with
good accuracy in the semi-arid Huangfuchuan River
Basin over the baseline period. The daily simulated
discharges at Huangfu station agreed well with in situ
Int. J. Climatol. (2014)
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 quantify the contributions from climate- and human-induced
factors to streamflow decrease by reconstructing the natural streamflow in the impacted period (1999–2006).
The results indicated that climate fluctuations and human
activities 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 on
the streamflow decrease in the Huangfuchuan River
Basin. The distributed biosphere hydrological modelling
approach and the above findings will be beneficial for
water resource management in the semi-arid or arid river
basins of China.
Acknowledgements
The study was funded by the National Natural Science Foundation of China (91125018 and 51009076),
the International Science and Technology Cooperation
Program of China (2010DFA21750) and the Chinese
Ministry of Water Resources Program (200901019). The
second author (Dr. Lei Wang) was financially supported
by the Hundred Talents Program of Chinese Academy of
Sciences. The authors are also grateful to two anonymous
reviewers whose comments are helpful in improving the
quality of this article.
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Int. J. Climatol. (2014)

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