Hydrologic alteration along the Middle and Upper East River

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Stoch Environ Res Risk Assess (2010) 24:9–18
DOI 10.1007/s00477-008-0294-7
ORIGINAL PAPER
Hydrologic alteration along the Middle and Upper East River
(Dongjiang) basin, South China: a visually enhanced mining
on the results of RVA method
Yongqin David Chen Æ Tao Yang Æ Chong-Yu Xu Æ
Qiang Zhang Æ Xi Chen Æ Zhen-Chun Hao
Published online: 28 November 2008
Ó Springer-Verlag 2008
Abstract This paper presents a visually enhanced evaluation of the spatio-temporal patterns of the dam-induced
hydrologic alteration in the middle and upper East River,
south China over 1952–2002, using the range of variability
approach (RVA) and visualization package XmdvTool. The
impacts of climate variability on hydrological processes
have been removed for wet and dry periods, respectively, so
that we focus on the impacts of human activities (i.e., dam
construction). The results indicate that: (1) along the East
River, dams have greatly altered the natural flow regime,
range condition and spatial variability; (2) six most
remarkable indicators of hydrologic alteration induced by
dam-construction are rise rate (1.16), 3-day maximum
(0.91), low pulse duration (0.88), January (0.80), July (0.80)
and February (0.79) mean flow of the East River during
1952–2002; and (3) spatiotemporal hydrologic alterations
are different among three stations along Easter River. Under
the influence of dam construction in the upstream, the degree
of hydrologic changes from Lingxia, Heyuan to Longchuan
Y. D. Chen Q. Zhang
Department of Geography and Resource Management
and Institute of Space and Earth Information Science,
The Chinese University of Hong Kong, Shatin,
Hong Kong, China
T. Yang (&) X. Chen Z.-C. Hao
State Key Laboratory of Hydrology-Water Resources
and Hydraulics Engineering, Hohai University,
210098 Nanjing, People’s Republic of China
e-mail: enigama2000@hhu.edu.cn; tfrank.yang@gmail.com
T. Yang
The Institute of Hydraulic Engineering of Yellow River,
450003 Zhengzhou, China
C.-Y. Xu
Department of Geosciences, University of Oslo, Oslo, Norway
station increases. This study reveals that visualization techniques for high-dimensional hydrological datasets together
with RVA are beneficial for detecting spatio-temporal
hydrologic changes.
Keywords Range of variability approach (RVA) Indicators of hydrologic alteration (IHA) Visual mining Dam construction Middle and Upper East River
1 Introduction
Human activities have directly altered the flow of large
rivers for thousands of years (Petts 1980). River systems
have been modified worldwide for flood control, navigation, water supply, power generation, and recreation.
Modification of both river and riparian habitats can range
from relatively localized effects of small-scale grazing to
broader effects of impoundment. As a result, many originally defining physical and ecological features of these
systems have been profoundly altered (Petts 1979, 1980;
Poff et al. 1997; Choi et al. 2005; Timme et al. 2005; Li
and Zhang 2008).
Altered flows have been one of the primary consequences of impoundments. An impoundment designed
primarily for flood control, navigation and water supply
tends to dampen natural flow variations by storing large
amounts of water for later, controlled release (Kojiri et al.
1989; Bravard and Petts 1996; Mathlouthi and Lebdi
2008). Conversely, dams built for power generation tend
to accentuate natural variability by creating daily high and
low flow periods to meet electrical demands. Many
researchers have developed indices to quantify flow
characteristics that are believed to be sensitive to these
human perturbations. Improved quantitative evaluations of
123
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2 Study area and data
The East River (Dongjiang), originating from Jiangxi
province and situated mainly in Guangdong province, is
one of the largest tributaries of the Pearl River (Zhujiang
River, 97°390 –117°180 E, 3°410 –29°150 N), which is the third
largest river basin in China (Fig. 1). The East River is
562 km long and has a drainage area of 27,040 km2,
accounting for about 5.96% of the Zhujiang River basin
(PRWRC 2006; Shi et al. 2005). Water resources in the
East River basin have been highly developed and heavily
committed for a variety of demands in flood control, water
supply, hydropower, navigation, irrigation, and suppression
of saltwater intrusion. The increasing municipal, industrial
and agricultural demands of water resources has created
substantial needs for further understanding of impacts of
hydraulic structures, such as impoundments, dams and
reservoirs, during the past 40 years. The construction of
large dams along the mainstream and major tributaries has
greatly altered the natural flow regime of the river. Presently, East River accounts for about 80% of Hong Kong’s
annual water supply (PRWRC 2006); therefore, spatial
patterns of hydrologic alterations of the East River water
resources systems will be greatly important for sustainable
social and economic development in the Zhujiang Delta
that is one of the economically developed regions in China.
A number of dams and reservoirs were built in the East
River basin during 1958–1974 for multiple purposes, such as
flood control, water supply and hydropower. Major dams and
Riv
er
The East River Basin
Fengshuba
Reservior
wu
Li
Riv
er
Longchuan
g R
fen
Xin
Xun
human-induced hydrologic changes have been widely
used to describe eco-environmental implications of
hydrologic alterations in supporting ecosystem management and restoration plans. Richter et al. (1996)
developed a method for assessing the degree of hydrologic alteration attributable to human disturbance within
an ecosystem. This method, referred to as the ‘‘Indicators
of Hydrologic Alteration’’ (IHA), is based either upon
hydrologic data available within an ecosystem or upon
model-generated data. Results of extensive study have
indicated that the range of streamflow regime is a primary
driving force in river ecosystems (Stanford and Ward
1996; Poff et al. 1997) and is an essential factor in sustaining aquatic environments (NRC 1992). Richter et al.
(1997) proposed the range of variability approach (RVA)
as the river management eco-targets, in which 33 hydrologic parameters (Richter et al. 1996, 1997; Richter and
Richter 2000) were used to assess hydrologic alterations
in terms of streamflow magnitude, timing, frequency,
duration and rate of change. Application of RVA for
assessing and mapping hydrologic alteration on a river
basin scale was demonstrated by evaluating the impacts of
dam construction on the hydrologic variability of two
major rivers in the upper Colorado River Basin in Colorado and Utah, USA (Richter et al. 1998).
RVA is regarded as a practical approach facilitating
river restoration planning, yet the published literature still
lacks in high-dimensional multivariate visualization in a
large variety of spatio-temporal IHA (Yang et al. 2008).
Thus, it is not convenient enough to detect spatio-temporal
variations, extremes, range, frequency, and patterns of
hydrologic alteration. In this regard, we present a visual
alternative method as a complement to RVA to assess
hydrologic alterations for the East River to tackle the
limitations mentioned above. The distinctive benefits of
this approach compared with those of traditional approaches (e.g., correlation and spectrum analysis, Mann–
Kendall trend test, regression technique, etc.) are that it can
offer interactive visualization of spatial and temporal
information for high-dimensional samples with limited
figures. From these figures more important spatio-temporal
behavior (e.g., extreme, variation, range, frequency, trend,
etc.) can be observed through the visually interactive
method. The objectives of this paper therefore are: (1) to
present a visually enhanced hydro-alteration assessment
method for streamflow of East River with data series
encompassing pre- and post-alteration periods; (2) to
determine the spatial behavior of 33 IHA factors which
features the hydrologic alteration in East River using parallel coordinates and glyph display techniques; and (3) to
evaluate the impact of dams on the hydrological alteration
along the East River, south China, using this visually
enhanced approach.
Stoch Environ Res Risk Assess (2010) 24:9–18
g
an
R.
ji
Xinfengjiang
Xingfengjiang
Reservoir
Reservior
ng
Do
Heyuan
ng
xia
Qiu
R.
Streamflow gauges
Lingxia
Reservior
Xizhi
R.
80 E
100 E
120 E 140 E
100 E
120 E
40 N
30 N
Basin outlet
The Pearl River Basin
20 N
500 km
Fig. 1 Location map of the Dongjiang basin and hydrological
stations
Stoch Environ Res Risk Assess (2010) 24:9–18
11
reservoirs here are the Fengshuba and Xinfengjiang dams
and reservoirs in the middle- and up-stream (Fig. 1). The
Fengshuba dam, located in the up-East River with a storage
capacity of 1,940 million m3 and a drainage area of
5,150 km2, was built in 1970 to reduce floods downstream
and provide water supply and hydropower as well (PRWRC
2006). The Xinfengjiang dam, the largest multi-functional
hydro-reservoir in Guangdong province with a storage
capacity of 13,980 million m3, was constructed during
1958–1962 (PRWRC 2006). Hence, to evaluate the potential
hydrologic alterations caused by large dam-construction,
this paper targets the study region in the middle and upper
East River basin to conduct the evaluation of dam-induced
hydrologic alteration, where other human activities (e.g.,
water diversion and sediment dredging) can be ignored.
Daily streamflow data from three gauging stations in the
East River basin were analyzed in the current study (Fig. 1;
Table 1 The streamflow gauging stations in the East River basin
Stations
Locations
Longchuan
115°150 E
Drainage
area (km2)
24°070 N
Series length
(year)
7,699
1952–2002
Heyuan
114°42 E
23°440 N
15,750
1951–2002
Lingxia
114°340 E
23°150 N
20,557
1953–2002
0
Table 1). In the basin, Longchuan is situated downstream
of the Fengshuba reservoir, and Heyuan and Lingxia are
located downstream of the Xinfengjiang reservoir. The
streamflow data are divided into pre- and post-dam periods
based on the construction period of the reservoirs (TNC
2001). Hence, the length of the daily mean flow record of
the pre- and post-dam period varied among gauging stations. Detailed information on the data is given in Table 1.
3 Methodology
3.1 Range of variability approach
The RVA uses 33 hydrologic parameters to evaluate potential hydrologic alterations (Richter et al. 1997; TNC 2001).
Of these parameters, sixteen hydrologic parameters focus on
the magnitude, duration, timing, and frequency of extreme
events and geomorphology; the other 16 parameters measure
the central tendency of either the magnitude or rate of change
of water conditions. Therefore, these 33 IHA parameters
provide a detailed representation of the hydrologic regime
for assessing hydrologic alterations. According to Richter
et al. (1997), these parameters can be categorized into five
groups addressing the magnitude, timing, frequency, duration, and rate of change (Table 2).
Table 2 Summary of Hydrologic parameters used in the RVA, and their features (Richter et al. 1997)
General group
Regime features
Streamflow parameters used in the RVA
Group 1: Magnitude of monthly water conditions
Magnitude, timing
Mean value for 12 calendar month
Group 2: Magnitude and duration of annual
extreme conditions
Magnitude, duration
Annual minimum 1-day means
Annual maximum 1-day means
Annual minimum 3-day means
Annual maximum 3-day means
Annual minimum 7-day means
Annual maximum 7-day means
Annual minimum 30-day means
Annual maximum 30-day means
Annual minimum 90-day means
Group 3: Timing of annual extreme
water conditions
Timing
Group 4: Frequency and duration of high
and low pulses
Magnitude, frequency duration
Annual maximum 90-day means
Julian date of each annual 1-day maximum
Julian date of each annual 1-day minimum
Number of high pulses each year
Number of low pulses each year
Mean duration of high pulses within each year
Mean duration of low pulses within each year
Group 5: Rate and frequency of water
condition changes
Frequency, rate of change
Means of all positive differences between
consecutive daily values
Means of all negative differences between
consecutive daily values
Number of rises
Number of falls
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Stoch Environ Res Risk Assess (2010) 24:9–18
The median, standard deviation, and range of these
parameters are computed with pre-dam daily flows. The
RVA target ranges of each hydrologic parameter are
decided by selected percentile thresholds or a simple
multiple of the parameter standard derivations for the
natural or pre-dam streamflow regime. The management
objective is not to ensure that the river attains the target
range every year; rather, it is to attain the range at the same
frequency as occurring in the natural or pre-dam flow
regime. For example, attainment of RVA target range
defined by the 25th and 75th percentile values of a particular parameter would be expected in only 50% of years.
The degree to which the RVA target range is not attained is
a measure of hydrologic alteration (HA). HA, expressed as
a percentage, can be calculated as:
Observed frequency Expected frequency
HAð%Þ ¼
Expected frequency
ð1Þ
Hydrologic alteration is equal to zero when the observed
frequency of post-dam annual values falling within the
RVA target range equals the expected frequency. A
positive value indicates that annual parameter values fell
inside the RVA target window more often than expected;
negative values indicate that annual values fell within the
RVA target window less often than expected.
3.2 Removal of potential impacts of climate variability
and change
Various potential impacts of climate variability and change
which had been mixed in the initial hydrological time series
in the study region must be removed in advance of the RVA
calculation. Generally, wet and dry years, which serve as the
indicator of climate variability and change and lead to highand low-flow years, respectively, can be considered for
separating the water years of hydrological time series to
retain the same sources of impacts, i.e., dam construction on
hydrologic regime. Yoo (2006) recommends that a proper
period in which annual basin precipitation more than
Pmean?0.75stdv (P C Pmean?0.75stdv) can be decided as a wet
year, whereas that whose annual basin precipitation less than
Pmean-0.75stdv is decided as a dry year (P C Pmean-0.75stdv).
The years with annual basin precipitation more than
Pmean-0.75stdv and less than Pmean?0.75stdv are considered as
normal years (Pmean-0.75stdv B P B Pmean?0.75stdv). Thus,
only streamflow records in normal-flow years are applied in
the RVA hydrological alteration assessment after excluding
the records in wet and dry years. The results of water year
(wet, normal and dry year) separation of the streamflow timeseries for the middle and upper East River are shown in Fig. 2
and Table 3. The middle-flow years (normal years) and the
123
Fig. 2 Water year separation of the streamflow time-series for the
middle and upper East River (The threshold of wet/dry year is
accordingly referred to the results and recommendation by Yoo 2006.
The shaded area represents the hydrological time-series containing in
normal year which will be used in the RVA analysis, the records in
wet year and dry year will be excluded out)
Table 3 The middle-flow years in the middle and upper East River
(Threshold: Pmean?0.75stdv = 1,471 mm, Pmean-0.75stdv = 1,891 mm)
No.
Year
Mean precipitation
(mm)
No.
Year
Mean precipitation
(mm)
1.
1952
1,518
17.
1979
1,736
2.
1953
1,739
18.
1980
1,717
3.
4.
1954
1955
1,821
1,498
19.
20.
1981
1982
1,849
1,664
5.
1957
1,703
21.
1984
1,634
6.
1960
1,790
22.
1985
1,784
7.
1961
1,883
23.
1986
1,592
8.
1964
1,832
24.
1987
1,747
9.
1965
1,623
25.
1988
1,557
10.
1966
1,862
26.
1989
1,531
11.
1968
1,865
27.
1990
1,475
12.
1970
1,702
28.
1994
1,735
13.
1972
1,714
29.
1995
1,574
14.
1974
1,733
30.
1996
1,526
15.
1976
1,796
31.
1998
1,672
16.
1978
1,781
32.
2000
1,690
time of dam construction used in the current study are listed
in Tables 3 and 4, respectively.
3.3 Visual approach on hydrologic alteration
evaluation
In many cases, visualization on the spatiotemporal environmental data can offer much more information than a
Stoch Environ Res Risk Assess (2010) 24:9–18
13
Table 4 The middle-flow water year (Table 3) of streamflow records
which are excluded the high-flow year (P B Pmean-0.75stdv,
1,471 mm) and the low-flow year (P C Pmean ? 0.75stdv, 1,891 mm)
to remove the impact of climate variability and changes. Crosshatched bars represent the pre-dam period, shades bars represent the
No.
Dam or reservoir
River
1.
Fengshuba
Mainstem of East River
2.
Xinfengjiang
Mainstem of East River
Pre-dam period
1952-69
1952-57
post-dam period. The total number of years of record available for
these pre- and post-periods are in parentheses within each bar. The
construction dates for each reservoir are identified within each
interlude between pre- and post-dam periods
Construction period
11 years
5 years
1970-74
1958-62
Post-dam period
1975-2002
1963-2006
(18 years)
(25 years)
(1) The hydrological dataset of Longchuan station will be separated into pre- and post-alternation periods in terms of the construction period
(1970–1974) of Fengshuba dam which is the neighborhood dam in upper stream to Longchuan station
(2) The hydrological dataset of Heyuan and Lingxia station will be separated into pre- and post-alternation periods with the total construction
period (1958–1974) containing Fengshuba and Xingfengjiang dam which influenced the natural hydrological regime of downstream collectively
table of numbers, which often leads to new insights and
decisions based on more information. Haan (2002) states
that because of the larger amounts of data available to
hydrologists, graphical display of the information is a
useful initial step in data analysis. Keim and Kriegel (1996)
note that visual data mining techniques have proven to be
of high value in exploratory data analysis and those techniques have a high potential for mining large datasets.
Reimann et al. (2001) report that multi-element and multimedium regional geochemical mapping has been found to
be a tool for understanding the cycling of elements in the
environment. Consecutive element maps are still the usual
way of visualizing spatial and temporal trends in the distribution of an element (Katrin 2005). A freeware
visualization tool for high-dimensional data XmdvTool
(1993) was chosen to display and explore datasets for
evaluating hydrologic alteration. In a scatterplot matrix,
which can be opened as an auxiliary display window, twodimensional scatterplots of all pairs of variables are plotted
(Fig. 3a). On the other side, each dimension corresponds to
an axis, and in a parallel coordinate display, the N axes are
organized as uniformly spaced vertical lines. A data element in N-dimensional space manifests itself as a
connected set of points (one on each axis) forming a
polyline (Fig. 3b). Brushing is a selection process in which
the user can highlight (select) or mask (hide) a subset of
data being graphically displayed by pointing at the data
elements, and brushing is associated with linking, which
means brushing data elements in one view affects the same
data in all other views (Fig. 3). The shape of the brush is an
N-dimensional hyperbox, and the user specifies N brush
dimensions using N slider bars. Brushes are displayed as
shaded regions, where data points which fall within the
brush are highlighted in a different color.
Fig. 3 Visualization of twodimensional point data as a
scatterplot (a) and in parallel
coordinates (b). A brush defined
in two dimensions (shaded
area) selects the highest four
values of variables A (ranging
from -0.50 to 10.50 in the
vertical coordinate) and B
(ranging from -1.00 to 21.00 in
the vertical coordinate) (black
points/polylines)
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Stoch Environ Res Risk Assess (2010) 24:9–18
4 Results
(A)
4.1 Evaluation of hydrologic alteration caused by dam
construction based on RVA approach
The subsequent assessment of hydrologic alteration from
three important aspects can be made:
(1)
(2)
(3)
Hydrologic alteration of mean monthly streamflow: it
can be seen from Fig. 4 that mean monthly streamflows along the river are altered differently by the
construction of dams. The results hereby demonstrate
the flood cutting-off adjustment of the Xinfengjiang
dam (Fig. 4b, c, Heyuan and Lingxia), the largest
multi-functional hydro-reservoir in Guangdong province with a remarkable storage capacity, exerted
considerable impacts on the mean July flow than the
remaining months of the year. Whereas, these effects
of Fengshuba dam with a smaller storage capacity are
less (Fig. 4a, Longchuan). Besides, the release of
runoff storage for irrigation and hydro-power in nonflood seasons obviously increases the mean monthly
flow of the East River. With help of RVA approach,
we further analyze the comprehensive hydrologic
changes of the River. The results (Table 5) show that
hydrologic changes of mean monthly flow in the
middle of flood period (July) and in winter (January,
February and March) are more obvious than other
months in the transition periods. Among the
12 months, a low change (0.18) of mean monthly
flow in August is observed, which could be due to
August is a transition month from flood period to dry
period in the region.
Hydrologic alteration of extreme values: The alteration of multi-day maximum (or minimum) is
represented by the change of the highest (or lowest)
multi-day average value of the year. High hydrologic
alterations of 7-, 30-, 90-day minimum, 1-, 3-, 7-day
maximum (Table 5; Fig. 5) suggest substantial environmental stress and disturbance on the East River
caused by dams.
Hydrologic alteration of frequency and duration of
high and low pulses: The pulsing behavior of the
East River has been severely affected, because both
high and low pulses occur with different frequencies,
where low pulses at Heyuan and Lingxia occur with
higher frequencies. The average duration of pulses,
on the other hand, is much shorter in the post-dam
period (Table 5; Fig. 5). This is a byproduct of
hydropower generation, wherein water is stored in
the reservoir until sufficient head is attained to
generate power efficiently, at which time the flow is
rapidly released through the dam turbines (Richter
123
(B)
(C)
Fig. 4 Hydrologic alterations of 12 monthly flow in the pre- and
post-dam period, the specific pre- and post period for each station can
be referred to Table 4
et al. 1996). The impacts of hydro-project on the
hydrologic regimes are elucidated by the greater
frequency of high and low pulses of lesser duration
and also the increase in the number of hydrograph
rises and falls.
Stoch Environ Res Risk Assess (2010) 24:9–18
Table 5 Summary of absolute
33 indicators of hydrologic
alternation for three stream
stations along the East River
(1) The dashes represent that
there are no observed events
falling inside the target range
(from Median -25% to
Median ?25%), thus they have
not been employed in the
evaluation of hydrologic
alternation
(2) Some mean values of 3
stream stations have not been
offered herein considering that
there are two dashes or above
existing for the specific item
(3) ‘–’ means the value exceeds
the valid ranges
15
IHA factors
Longchuan
Heyuan
Lingxia
Mean value (3 stations)
1. January
0.72
0.92
0.77
0.80
2. February
0.91
0.69
0.77
0.79
3. March
0.81
0.84
0.66
0.77
4. April
0.72
–
–
–
5. May
0.81
–
–
–
6. June
0.44
0.77
0.71
0.64
7. July
0.63
0.87
0.89
0.80
8. August
0.34
0.07
0.14
0.18
9. September
0.68
0.84
0.02
0.51
10. October
0.16
0.38
0.66
0.40
11. November
0.44
0.53
0.77
0.58
12. December
0.03
0.61
0.77
0.47
13. 1-day minimum
0.63
0.46
–
0.55
14. 3-day minimum
0.34
0.77
–
0.56
15. 7-day minimum
16. 30-day minimum
0.63
0.72
0.77
0.69
–
–
0.70
0.71
17. 90-day minimum
0.63
0.92
–
0.78
18. 1-day maximum
0.53
0.92
–
0.73
19. 3-day maximum
0.91
0.92
0.89
0.91
20. 7-day maximum
0.16
0.92
0.71
0.60
21. 30-day maximum
0.16
0.77
0.89
0.61
22. 90-day maximum
0.22
0.53
0.20
0.32
23. Number of zero days
0
0
0
0
24. Base flow index
0.53
–
0.94
0.74
25. Date of minimum
0.91
0.92
0.37
0.73
26. Date of maximum
0.53
0.61
0.66
0.60
27. Low pulse count
0.58
0.77
0.77
0.71
28. Low pulse duration
0.93
–
0.83
0.88
29. High pulse count
0.53
0.81
0.66
0.67
30. High pulse duration
0.64
0.48
0.77
0.63
31. Rise rate
32. Fall rate
1.53
–
1.33
–
0.61
1.16
–
33. Number of reversals
–
–
–
–
Mean value
0.57
0.71
0.64
0.64
In summary, the six most remarkable dam-induced
impacts factors are the rise rate (1.16), 3-day maximum
(0.91), low pulse duration (0.88), January (0.80), July
(0.80), February (0.79), whereas the seven most slight daminduced impact factors are August (0.18), 90-day maximum (0.32), October (0.40), December (0.47), September
(0.51), 1- and 3-day minimum(0.55 and 0.56) in the middle
and upper East River, South China, during 1952–2002. The
variability of monthly mean flow, extremely low water
conditions, timing of annual high and lows, high and low
pulse durations, and frequency and rate of hydrograph rises
and falls in summer and winter, has been reduced along the
middle and upper East River. On the other hand, increased
coefficients of variation and flow magnitude in dry season
can be observed.
4.2 A visual explanation of extreme indicators
of hydrologic alteration
(1)
Visualization of hydrologic alteration through parallel-coordinates display
Within this study, a parallel-coordinate display of the
combined indicators of hydrologic alteration dataset for the
middle and upper East River is shown in Fig. 6. The degree
of hydrologic alteration of each station is plotted on the
123
1.5
1.0
Longchuan
Heyuan
75%Percentlie=0.77
Lingxia
Meanvalue(3 stations)
25%Percentlie=0.56
0.5
ru
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ar
ch
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ri
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o
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e
3- y m em r
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im r
7- y
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30 ay in m
-d m im
90 ay in um
-d m im
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1- ay ini m
d m m
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3- y ini m
d m m
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7- y xi m
d m m
30 ay ax um
-d m im
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u
m -da ma u
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D se ro um
at fl d
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D e o w ay
s
at f
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ls im
H uls e um
H igh e co
ig
d
u
h pu ur nt
a
p
u lse tio
ls
e co n
d
u un
R ra t
is tio
e n
F ra
al te
lr
at
e
0.0
F
eb
Fig. 5 Degrees of Indicators of
Hydrologic Alteration for three
streamgauges on the middle and
upper East River
Stoch Environ Res Risk Assess (2010) 24:9–18
Degrees of Indicators of Hydologic Alternation
16
Fig. 6 Parallel coordinate display for 6 highest indicators of hydrologic alteration at three stream stations of the East River (1952–2002).
Each item is represented by a polyline connecting vertical axes (in
light grey), black lines represent indicators of hydrologic alternation
at the selected Heyuan station, the other unselected stations were
drawn in light grey. The shaded area consists of 50 highlighted
polylines, each of them represents a set of 6 IHA degrees of Heyuan
gauge in specific year (1952–2002). There are 150 polylines to
describe the features of 6 IHA degrees for three sites in the East River
vertical axes and each in the six most remarkable indicators
of hydrologic alteration (January, February, July, 3-day
maximum, Rise rate and 90-day minimum) forms a line
(polyline). In Fig. 6 hydrologic alterations for Heyuan
station are selected (black polyline) to visualize the variability of 6 hydrologic alteration items at the Heyuan
station. It can be inferred that the black polyline shows the
lowest value of flow in February and 3-day maximum and
highest value in January. The simplicity of interactive
brushing promotes visual queries, as different indictors,
and sites can be brushed and localized in both space and
time.
123
(2)
Spatial patterns of hydrologic alteration implied by
glyph display
Figure 7 illustrates different spatial patterns of degrees
of hydrologic alteration at 3 stations along the middle and
upper East River induced by dam operations from 1952 to
2002. Each direction with a different length of a specific
glyph represents an IHA factor with a different value (1–
Stoch Environ Res Risk Assess (2010) 24:9–18
Fig. 7 Glyph display for six highest indicators of hydrologic c
alteration at three streamflow stations of the East River (1952–
2002). a Longchuan station, b Heyuan station, c Lingxia station. Each
number (1–33) on the edge of the circle corresponds to a IHA factor
as is shown in Table 5.
17
(A)
32
1
33
2
3
31
4
30
5
29
6
28
7
27
33), which indicates the specific IHA factor listed in
Table 5. Thus we can easily examine the degree of
hydrologic changes from the direction and length of glyph.
The slimmest glyph (Fig. 7a) implies that the impact on
natural flow regime of Longchuan station imposed by the
Fengshuba dam is the slightest among the three stations.
Therefore, the overall degree of hydrologic change at
Longchuan station is the lowest in the middle and upper
East River. The most full-grown glyph suggests the
remarkable degree of hydrologic alteration at Heyuan station and Lingxia station (Figs. 7b, c), perturbed by the
largest dam—Xinfengjiang and Fengshuba. Comparison of
Fig. 7b with c reveals that the impact of dam construction
of Xinfengjiang on Lingxia is smaller than that on Heyuan
for extreme values (e.g., number 15–18, Fig. 7b, c). This is
because Lingxia station, with a long distance from
Xinfengjiang (Fig. 7c), receives the natural confluence
from the Qiuxiang River and district area (Heyuan to
Lingxia).
8
6
9
5
1
24
11
23
12
22
13
21
14
20
19
18
17
16
15
(B)
5 Conclusion and discussion
The influence of dam construction on hydrological regimes
of the East River was systematically studied with the help
of visual RVA-based method. Some interesting conclusions can be summarized as follows:
1)
2)
3)
(C)
Along the East River, dams have greatly altered the
natural flow regime, range condition, and spatial
variability.
Six most remarkable indicators of hydrologic alteration induced by dam-construction are the rise rate
(1.16), 3-day maximum (0.91), low pulse duration
(0.88), January (0.80), July (0.80) and February mean
flow (0.79) of the middle and upstream East River
during non-flood seasons (1952–2002); whereas other
27 IHA factors remain moderate hydro-changes (percentiles \75%).
The spatio-temporal hydrologic alterations are different among the three stations. It is implied that the
overall degree of hydrologic change at Longchuan
station is the lowest in the East River. The highest
degree of hydrologic alteration is at Heyuan station,
123
18
Stoch Environ Res Risk Assess (2010) 24:9–18
perturbed by the largest dam—Xinfengjiang in Guangdong province and Fengshuba reservoir upstream as
well. Lingxia station, receiving flow from the natural
confluence of the Qiuxiang River and district area
(Heyuan to Lingxia) exhibits a moderate degree of
hydrologic alteration.
Construction and operation of the reservoirs, with the
aim to reduce flood disaster and sediment deposition, have
inevitably caused significant hydrologic alterations, which
severely changed the balance of natural eco-flow regime
with substantial threat to wild species. With the alternative
visual enhanced RVA method, the spatial variation and
extremes of hydrologic alterations caused by dam construction in the East River over recent five decades was
visually assessed and mined, which can reveal more
behavior through spatial and temporal trends of hydrologic
alterations than the traditional approach and can make
results more convincing and effective to recognize the
hydrological patterns and trends. The results of this study
will be greatly helpful for the future management of water
resources and will be greatly important for further understanding of the human impacts (e.g., hydro-dam projects)
on hydrological regimes in the East River. Furthermore, it
is significant for sustainable social and economic development in the Pearl River (Zhujiang) Delta which is one of
the economically developed regions in China, south China.
Acknowledgments The work described in this paper was supported
by a key grant from the Chinese Ministry of Education (308012), key
grant from the National Natural Science Foundation of China
(40830639), a grant from the Research Grants Council of the Hong
Kong Special Administrative Region, China (CUHK4627/05H), an
open research grant from the Key Sediment Lab of the Ministry for
Water Resources (2008001), a National Key Technology R&D Program (2007BAC03A060301), and the Program of Introducing Talents
of Discipline to Universities—the 111 Project of Hohai University
(B08048). Cordial thanks should be extended to the Nature Conservancy, USA for the ‘Indicators of Hydrologic Alteration’ (IHA)
software used in RVA computation, the XmdvTool for visualization
of high-dimensional hydro-data developed by The University of
California, Davis, and the Department of Water Resources and
Environment, Sun Yat-sen University for providing hydrologic data
of study area. Prof. V. P. Singh from Texas A&M University has
kindly read and improved the quality of the final version of the paper.
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