1. Introduction
The river system is the aorta of the Earth and plays a key role in the
global water cycle, local climate change, and ecological balance. In
recent years, the degradation of river ecology has occurred due to
climate change and intensified human activities (Johnson et al., 2009;
Li, Liang, Xiao, Cao, & Hu, 2019; Palmer et al., 2009; Stevenson &
Sabater, 2010). The most important human activities are the construction
of water conservancy projects such as reservoirs and dams, which have an
impact on downstream ecology by changing the hydrological regime (N
Leroy, Olden, Merritt, & Pepin, 2007; Yin, Yang, & Petts, 2011).
Globally, about 70% of all rivers are dispersed by large
reservoirs(Christer, Reidy, Mats, & Carmen, 2005), and according to the
latest data from the International Commission on Large Dams (ICOLD),
there are 57,985 large reservoirs in the world, of which more than 40%
are distributed in China. The impact of reservoirs depends mainly on the
ratio of their capacity to natural streamflow, functions, and regulation
rules (Batalla, Gómez, & Kondolf, 2004; Williams & Wolman, 1984).
While realizing flood control, power generation, water supply, and other
functions, the reservoir also blocks the natural flow of rivers, causing
changes in hydrological processes and dynamics, which will have a direct
impact on river biodiversity and ecological functions (N Leroy et al.,
2007; Yang, Cai, & Herricks, 2008). Reservoir-induced hydrological
alterations and their ecological impact has attracted more and more
attention from ecologists, hydrologists, and policy-makers.
To evaluate the ecological impacts of the reservoir, indicators are
required to quantify the extent of hydrological alteration. Olden &
Poff (2003) summarized more than 170 hydrological indicators, but there
exists a clear correlation between them and statistical redundancy.
Brian D Richter, Baumgartner, Powell, & Braun (1996) established
Indicators of Hydrologic Alteration (IHA) for assessing hydrological
alterations, which is widely used. The IHAs are divided into five
categories: monthly mean flow, magnitude and duration of annual extreme
flow, timing of annual extreme water, frequency and duration of high and
low pulses, and rate and frequency of flow condition changes. There are
a total of 32 indicators, which were later revised to 33(Brian D.
Richter, Baumgartner, Braun, & Powell, 1998). Based on the IHA, a
series of methods such as the range of variability method (RVA) and the
Dundee hydrological regime alteration method (DHRAM) were developed to
quantitatively assess the degree of hydrological alteration and the
ecological risk to rivers of hydrological alterations (Black, Rowan,
Duck, Bragg, & Clelland, 2005; B. Richter, Baumgartner, Wigington, &
Braun, 1997; Shiau & Wu, 2007).
Compared with the 170 indicators, the 33 IHAs are more simplified, but
the correlation between the indicators has not been well resolved, which
may complicate the assessment of ecological flows (B. Gao, Yang, Zhao,
& Yang, 2012; Y. Gao, Vogel, Kroll, Poff, & Olden, 2009; Q. Zhang, Gu,
Singh, & Chen, 2015). Vogel et al., (2007) proposed dimensionless
eco-flow indicators based on the flow duration curve (FDC) and the
ecodeficit and ecosurplus, which can directly reflect the surplus or
deficit of river ecological flows. In this work, these two indicators
are called eco-flow indicators. The introduction of the ecodeficit and
ecosurplus provides a new direction for hydrological research under the
influence of reservoirs; however, owing to the limited research on this
topic, it is not clear whether the eco-flow indicators can better
reflect the degree of change of streamflow time series. Furthermore, the
relationship between these eco-flow indicators and traditional
hydrological indicators is also unclear.
In addition, the research on hydrological alteration and eco-flow under
the influence of reservoirs mainly focuses on large river basins (B. Gao
et al., 2012; Y. Gao et al., 2009; Kroll, Croteau, & Vogel, 2015; Q.
Zhang, Zhang, Shi, Singh, & Gu, 2018). Large-scale watersheds usually
span multiple climatic zones and are characterized by strong underlying
surface changes; therefore, the impact of a reservoir on hydrological
alteration is difficult to extract from these changes. The Taizi River,
which has a relatively stable climate and underlying surface is a
suitable site for this research. The Shenwo Reservoir (SWR) in the
middle reaches can be considered the most important cause of the
hydrological regime and ecosystem variation in this basin. Previous
studies on the Taizi River have focused on the annual or seasonal
changes in streamflow, with little attention being given to the
hydrological alterations and their ecological impact (G. Q. Wang et al.,
2011; Y. Zhang, Wang, & Wang, 2012). Therefore, the objectives of this
study were as follows: (1) to analyze the reservoir-induced surplus and
deficit of eco-flow using two eco-flow indicators, the ecosurplus and
ecodeficit; (2) evaluate the reservoir-induced hydrological alteration
and its ecological impact; and (3) compare eco-flow indicators with
IHAs.
2. Materials and methods
2.1 Study area
The Taizi River Basin (TRB), which is located in the Liaoning Province
of China, originates from Changbai Mountain and has a length of 413 km.
It flows through Benxi City, Liaoyang City, and Anshan City, with a
drainage area of 13,883 km2 (Figure 1). The TRB has a
temperate monsoon climate, with an average annual precipitation of
700–900 mm that is mainly concentrated in July–September, which
account for 70% of the annual precipitation. Under natural conditions
(before the construction of the SWR), the average annual streamflow was
44.96 × 108 m3, and the monthly
distribution is shown in Figure 2. To meet the water supply needs of
industry, agriculture, and living, many reservoirs have been built in
TRB, the most important of which is the SWR, which was built in
1970–1974, with a control area of 6175 km2 and a
storage capacity of 7.91 × 108 m3.
2.2 Data
The lower reaches of the TRB are a typical agricultural plain with
several water intakes along the river. Therefore, the Liaoyang Station
in the middle reaches of the TRB was selected as the study station. In
addition, the Liaoyang Station, which is located 25.5 km downstream, is
the closest hydrological station to the SWR; therefore, the flow data
can directly reflect the SWR regulation. The daily streamflow data from
1961 to 2016 were obtained from the Liaoning Provincial Department of
Water Resources. The monthly average precipitation data of 21 rain
gauging stations (Figure 1) from 1961 to 2016 were obtained from the
China Meteorological Data Network (data.cma.cn ). The
precipitation of the drainage area controlled by Liaoyang Station was
calculated by the Thiessen Polygon method.
2.3
Methods
2.3.1 Ecosurplus and
ecodeficit
Two generalized indicators, ecosurplus and ecodeficit, based on the FDC,
can reflect the overall gain or loss, respectively, in streamflow that
results from flow regulation during a period (Vogel et al., 2007). The
FDC illustrates the percentage of time (P i) a
given streamflow (Q i) was equaled or exceeded
during a specified period of time (Vogel & Fennessey, 1995). WhenQ i is arranged in descending order,P i can be expressed as
P i = i /(n +1) (1)
where i is the rank of Q i, and n is
the sample size of Q i. Since the introduction of
ecosurplus and ecodeficit, a variety of different calculation methods
have been developed (B. Gao et al., 2012; Vogel et al., 2007; Y. Wang,
Wang, Lewis, Wu, & Huang, 2017). As eco-flow varies within a certain
range, this study defined the range as the upper and lower limits of
eco-flow before the reservoir was constructed, which is different from
the original definition (Vogel et al., 2007). First, construct the
pre-reservoir annual or seasonal FDCs of each year; then, calculate the
25%, 50%, and 75% quantiles of Q i
corresponding to each P i. Then, construct 25%,
50%, and 75% of the FDC. In this study, 75% and 25% of the FDC were
used as the upper and lower limits of eco-flow. The ecosurplus and
ecodeficit are calculated as
Ecosurplus = A s/A 50%
(2)
Ecodeficit = A d/A 50%
(3)
where A s is the area enclosed by the 75% of the
FDC and the portion of the FDC higher than 75% of the FDC in the
specified year (or season), A d is the area
enclosed by 25% of the FDC and the FDC lower than 25% of the FDC, andA 50% is the annual or seasonal flow
corresponding to 50% FDC, as shown in Figure 3. Rather than being
divided by the mean annual or seasonal flow (B. Gao et al., 2012; Q.
Zhang et al., 2015), this calculation is divided by the flow
corresponding to 50% of the FDC (shaded part in Figure 3). As the
median is less affected by extreme events relative to the mean, the
ecological indicators can be better quantified (Y. Wang et al., 2017).
2.3.2 Evaluation of hydrological
alteration
The 33 IHAs were used to assess the specific impact of the reservoir on
hydrological alterations. As there is no ”zero-flow day” in the
streamflow data, the 32 IHAs are finally used, as shown in Table 1. Each
indicator has its ecological relevance (The Nature Conservancy, 2009).
The most ecologically relevant hydrological indicators was identified
from the 32 IHAs by principal component analysis (PCA) to evaluate the
hydrological alteration. According to the Kaiser–Guttman criterion,
components with eigenvalues greater than 1 were retained (Jackson,
1993), and the IHA parameter with the largest load was selected in each
principal component (PC) to represent each PC.
2.3.3 Evaluation of
biodiversity
The Shannon Index (SI ) is the most widely used indicator for
assessing biodiversity (Shannon & Weaver, 1959). The larger theSI , the richer the biodiversity (Pettersson, 2015; Yang et al.,
2008). The SI is expressed as
(4)
where p i is the ratio of the i-th species to the
total number of species in a community. Yang et al., (2008) determined a
quantitative function between SI and the 32 IHAs based on genetic
programming:
(5)
where D min represents the date of minimum flow in
a year, Q 7daymin andQ 3daymin represent the 7-day minimum and 3-day
minimum flows, respectively, Q 3daymax represents
the 3-day maximum flow, Q 3 andQ 5 represent the mean flow for March and May,
respectively, and R rate represents the rise
rates. Owing to the lack of data on the biodiversity in TRB, the
biodiversity can be estimated initially according to formula (5).
3. Results and discussion
3.1 Variations in eco-flow
indicators
3.1.1 Changes in flow components from the perspective of
eco-flow
indicators
The construction of a reservoir will cause changes in the flow
components of downstream rivers, such as high and low flow components.
Figure 4 shows the pre-reservoir and post-reservoir FDC scatters in
years and seasons. The values and frequencies of post-reservoir
high-flow are smaller than those of the pre-reservoir high-flow, and the
high-flow below 25% FDC will lead to an increase in the ecodeficit. The
post-reservoir low-flow component is higher than that pre-reservoir
low-flow,and the low-flow above 75% FDC will produce an eco-surplus.
The changes in flow components are closely related to the seasonal
regulation of the reservoir. As the streamflow of autumn accounts for
the largest proportion of annual streamflow (Figure 2), the change in
high-flow after construction of the SWR (the decreases in value and
frequency) is mainly reflected in the autumn, while the low-flow
component can better cover the areas where low-flow occurs before the
reservoir, which inevitably leads to an increase in the eco-deficit in
autumn. The post-reservoir FDC changes in spring and winter are similar,
showing a situation where the peak is shifted down, while the tail is
moved up. Furthermore, the difference between high and low flows in most
years reduced. The values and frequency of the post-reservoir summer
high-flow and low-flow component are higher than those of the
pre-reservoir flow component, which is related to the water demand for
irrigation in the downstream paddy fields in summer and will inevitably
lead to an increase in the ecosurplus and a reduction in the ecodeficit.
The change in the FDC scatters plot can preliminarily determine the
impact of the reservoir on eco-flow indicators. However, the variations
in the eco-flow indicators are also closely related to precipitation
anomalies.
3.1.2 Relationship between eco-flow indicators and
precipitation
Figure 5 shows the annual and seasonal eco-flow indicator variations and
precipitation anomalies before and after construction of the SWR, and
Table 2 shows the correlation coefficient between the two. From the
annual scale, the changes in the ecosurplus and precipitation are
relatively consistent in the entire period (correlation coefficient is
0.79), especially in the year when a considerable amount of rain is
received. As the precipitation increases the high-flow component of the
annual runoff, the part of the FDC exceeding 75% of the FDC increased,
leading to increased ecosurplus. However, it was observed that the
extremely rainy years following the construction of the SWR could not
produce a correspondingly large ecosurplus, such as 1985, 1995, and
2010, which indicates that as the precipitation increases to a certain
extent, the resulting ecosurplus is limited by the reservoir and reaches
a certain upper limit, which is between 0.9 and 1.3. In successive dry
years, such as 1976–1984 and 1996–2009, the ecosurplus is basically
zero, and the rainy year following the successive dry years shows a
relatively small ecosurplus, which is mainly affected by reservoir
impoundment. Compared with the ecosurplus, because the overall drought
that is experienced after the construction of the reservoir leads to an
increase in the fraction below 25% FDC, the post-reservoir ecodeficit
is higher than the pre-reservoir ecodeficit. The pre-reservoir
ecodeficit was well correlated with precipitation. However, affected by
the reservoir, the correlation becomes worse, especially in years during
which a drought was experienced, such as 1979, 1989, and 2014, as the
ecodeficit is not proportional to the drought regime. This is because
the opening and discharging of water by the reservoir to meet the
downstream water demand increased the ecodeficit.
Compared with the variation on the annual scale, the difference between
the two increases on the seasonal scale. The changes in autumn, during
which the most precipitation is experienced, were most similar to the
changes on the annual scale. The eco-flow indicators and precipitation
were in good agreement, and the correlation coefficients were 0.76 and
-0.60, respectively (Table 2). However, the consistency of the eco-flow
indicators and precipitation in spring, summer, and winter is poor, with
correlation coefficients that are less than 0.3, indicating that the
ecosurplus and ecodeficit are significantly affected by the construction
of the SWR. In spring and summer, the river is mainly characterized by
ecosurplus, even in the extreme dry or successive dry period. A
significantly decreased ecodeficit was observed after the construction
of the reservoir, reflecting the role of the reservoir in maintaining
the low flow in spring and especially in summer (Figure 4). In winter,
the trends of the ecosurplus and ecodeficit are similar to those in
autumn, and to some extent inherit the characteristics of eco-flow
indicators in autumn. As the temperature in winter is so low that
snowfall cannot form a runoff, the correlation between the eco-flow
indicators and precipitation is poor. The eco-flow indicators in winter
may be controlled by autumn precipitation and reservoir regulation.
3.2 Hydrological regime alteration using the IHA
method
Table 3 lists the changes in 32 IHAs before and after the construction
of the reservoir, with 24 IHAs exhibiting a relative change rate of more
than 20%, indicating that the reservoir has a greater impact on the
hydrological regime. The average monthly flow in spring and summer is
generally increased, which is consistent with the upward trend of the
seasonal FDC in Figure 4 and the increase in the ecosurplus in Figure 5.
The streamflow in autumn showed a decreasing trend, which was consistent
with the downward trend of the autumn FDC in Figure 4 and the increase
in the ecodeficit in Figure 5. Even though the post-reservoir climate
tends to be arid, under the regulation of the SWR, the streamflow during
the dry season (spring and winter) still increased from 8.89-22.81
m3/s to 19.49-24.41 m3/s, which
increased the 1-, 3-, 7-, and 30-day minimum flow, leading to an
increase in the low-flow component value, which is consistent with the
change in the spring FDC in Figure 4. On the other hand, the regulation
of the SWR significantly reduced the flow fluctuation during the wet
season, from 116.86-276.87 m3/s to 120.62–148.98
m3/s, which reduced the 1-, 3-, 7-, 30- and 90-day
maximum flow significantly, leading to a decrease in the high-flow
component, which is consistent with the change in the autumn FDC in
Figure 4. The base flow index (BFI) increased by 45%, but this was not
due to an increase in actual base flow; it was the result of reservoir
regulation. The date of maximum flow did not change significantly, but
the date of minimum flow was postponed by 22%. The low and high pulse
counts increased, while the duration decreased, which is closely related
to the hydropower generation of the SWR (Q. Zhang, Xiao, Liu, & Singh,
2014), and stabilized the downstream hydrological processes to a certain
extent. Significant changes in the rise and fall rates, and the number
of reversals were not observed. Overall, the changes in the 32 IHAs
coincide with the changes in the FDC and eco-flow indicators, which
gives us more confidence using the ecosurplus and ecodeficit to evaluate
ecological flow. The relationship between the 32 IHAs and the ecosurplus
and ecodeficit will be discussed later.
3.3 Impact of hydrological alteration on downstream
biodiversity
3.3.1 Ecologically relevant hydrological indicators
Similar to other hydrological indicators, IHAs are interrelated (Olden
& Poff, 2003). Figure 6 is a boxplot of the correlation coefficient
between each IHA parameter and the remaining 31 IHAs. The absolute value
of the correlation coefficient is between 0.003 and 0.995, with an
average value of 0.33. It should be noted that some values are even
larger than 0.9, which illustrates the statistical redundancy of the 33
IHAs. Therefore, it is important to determine a small set of
ecologically relevant hydrological indicators (ERHI) to characterize
changes in the eco-flow regime. The results from the PCA method are
shown in Table 4. Six principal components (PC) are selected, which are
the 30-day minimum, BFI, mean flow in May, high pulse duration, number
of reversals, and date of minimum. The selected six ERHIs are
distributed in five IHA groups (Table 1); therefore, the ERHIs can
reflect the five characteristics of hydrological regime and are poorly
related to each other. The results were similar to those of (Yang et
al., 2008; Q. Zhang et al., 2015; Q. Zhang et al., 2018)
The variations in the ERHIs over time are shown in Figure 7, and the
trend is fitted by locally weighted regression (Loess). After the
construction of the SWR, the mean flow in May increased significantly,
with an average increase of 143% (Table 3) and remained high. May is
the migratory and spawning period of the fish in the TRB. Dugan et al.,
2010; Larinier, (2001) stated that when the flow rate of the river
exceeds the natural condition, the migration distance and velocity will
decrease as the flow rate increases, and migration difficulties will
occur. Obviously, the increase in flow in May will have an adverse
impact on the migration and reproduction of fish in the TRB. The 30-day
minimum flow oscillating amplitude significantly increased after 1985,
and the BFI showed an increasing trend, with a change range that also
increased. The change in minimum flow will rebalance the competitive,
ruderal, and stress-tolerant organisms. The minimum flow before the
construction of the SWR occurred mainly in the spring, and in winter and
summer after the construction of the SWR. In general, the minimum flow
day presents a postponement, which will have a potential impact on river
ecology, especially the reproduction of aquatic organisms (Q. Zhang et
al., 2018).After the construction of the SWR, the high pulse duration
was significantly shorter and remained at a lower level, with an average
reduction of 30% (Table 2), which directly influences the bedload
transport, channel sediment textures, and duration of substrate
disturbance, and reduces the time for nutrient and organic matter
exchanges between the river and floodplain. The number of reversals
slightly increased, which may cause slight stress on low-mobility
streamedge organisms. Changes in ERHIs after the construction of the SWR
will cause changes in the ecosurplus and ecodeficit, leading to changes
in the river ecosystem (Q. Zhang et al., 2015).
3.3.2 Ecological diversity indicator:SI
Figure 8 shows the temporal variation in the ecological diversity indexSI , and regression fitting was performed by the Loess function.
After the SWR was constructed, the SI was generally smaller,
indicating the negative impact of the SWR on the downstream river
ecosystem. In the natural situation, changes in precipitation directly
control changes in flow, indirectly affecting biodiversity. Figure 9
shows the relationship between precipitation and SI before and
after the SWR was constructed. A good correlation between precipitation
and SI was observed, with R2 = 0.79, andSI increased with an increase in precipitation. However, the
correlation decreased after the SWR was constructed, with
R2 = 0.042, indicating that the increase in
precipitation was not enough to cause an increase in ecological
diversity. This also highlights the negative impact of the construction
of the SWR on the ecological changes of downstream rivers. As indicated
in 3.3.1, the SWR can affect the downstream ecology by changing the
hydrological regime, but the changes in the hydrological regime are not
the only explanation. On the one hand, the SWR blocks the migratory
channels of migratory fish and affects the interspecific gene flow of
the entire system. On the other hand, the water discharged from the
reservoir originates from the deep part of the reservoir. In summer, the
temperature is lower than that of the river, while in winter, it is
higher than that in the river. This will change the living environment
and life cycle of aquatic organisms, as the processes of reproduction
and hatching depend on the temperature change. In addition, a large
amount of gravel is intercepted, which causes invertebrates at the
bottom of the riverbed, such as mollusks and shellfish, to lose their
living environment. Although the SI decreased after the SWR was
constructed, the change range was smaller than that before the SWR was
constructed, and the downstream river ecology exhibited a new
equilibrium state, where the ecological diversity was less affected by
precipitation and mainly controlled by the regulation of the SWR.
3.4 Comparison between ecological indicators and IHA
indicators
To understand the relationship between the eco-flow indicators and 32
IHAs, the correlation coefficients between them were calculated, as
shown in Table 5. It can be seen that there is a good correlation
between most eco-flow indicators and the 33 IHAs. The ecosurplus is
positively correlated with most of the 32 IHAs, while the eco-deficit is
negatively correlated with the IHAs. Each eco-flow indicator is
significantly correlated with 9 to 21 IHAs, and most IHAs are also
significantly correlated with at least one ecological flow indicator. In
this respect, eco-flow indicators have a smaller statistical redundancy
than the 32 IHAs. The monthly mean flow of each season is highly
correlated with the corresponding seasonal eco-flow indicators, which
indicates that the seasonal eco-flow index can reflect the variation in
monthly streamflow. In addition, the correlation between extreme flow
and eco-flow indicators is obvious. The minimum flow is most strongly
correlated with the spring ecosurplus, while the maximum flow is
strongly correlated both with the ecosurplus in autumn and the year. As
the minimum flow occurs mostly in spring, the BFI is related to the
spring eco-flow index. The rise rate is positively correlated with the
ecosurplus and negatively correlated with the ecodeficit, while the fall
rate exhibits an opposite trend.
These correlations indicate that eco-flow indicators can reflect
information on changes in hydrological alteration on smaller time
scales. As the eco-flow indicator is based on the annual or seasonal
FDC, and the FDC cannot reflect the duration, time, and variation in a
specific runoff event, the correlations between the eco-flow indicator
and date of minimum, date of maximum, count, and duration of low and
high pulses, and the numbers of reversals are quite weak.
In general, the eco-flow indicators can reflect most of the IHA
information and its changing characteristics; therefore, the eco-flow
indicators can provide a good evaluation standard for hydrological
regime changes. In addition, the calculation of the ecosurplus and
ecodeficit is based on the FDC and independent of the IHA, which can
effectively solve the statistical redundancy between the hydrological
indicators. Therefore, the combination of the eco-flow indicators and
IHA can effectively reflect the hydrological alteration caused by
reservoirs; at the same time, it can guide the operation of reservoirs
to protect river ecology.
4. Conclusions
Based on the daily flow data from 1961 to 2016 in Liaoyang Station on
the lower reaches of the SWR, the variations in ecosurplus and
ecodeficit were analyzed. Together with the 32 IHAs, the ERHIs, andSI , the results were used to evaluate the impact of the
construction of SWR on downstream hydrological alteration and ecological
diversity. The main findings are as follows:
(1) Eco-flow indicators (ecosurplus and ecodeficit) can be used to
analyze the annual and seasonal eco-flow variation due to the
construction of the reservoir. After SWR was constructed, the high-flow
value and frequency decreased, especially in autumn, which made the
high-flow lower than 25% FDC, resulting in an ecodeficit; the
post-reservoir low-flow value significantly increased, making the
low-flow above 75% FDC and producing a ecosurplus, especially in spring
and summer.
(2) The relationship between the eco-flow indicators and precipitation
can reflect the main factors affecting the eco-flow indicators. The
annual ecosurplus is less affected by the reservoir, while the
ecodeficit is greatly affected by the reservoir. Moreover, the seasonal
ecological indicators are greatly affected by the reservoir, especially
in spring and summer.
(3) After the SWR was constructed, the 32 IHAs showed significant
changes, which were consistent with the changes in the ecosurplus and
ecodeficit. Six ERHIs were screened by PCA, and their changes reduced
the downstream ecological diversity. The ecological diversity was mainly
controlled by reservoir regulation, and a new equilibrium state
appeared.
(4) The eco-flow indicators have a good correlation with the 32 IHAs,
and can reflect the change information of most IHAs. Therefore, the
eco-flow indicators can reflect the changing characteristics of the
hydrological regime under the influence of the reservoir and provide a
good evaluation standard. At the same time, the calculation of the
ecosurplus and ecodeficit is independent of IHA, thus avoiding
statistical redundancy.