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\begin{document}
\title{Impact of reservoir on downstream runoff and baseflow recession
characteristics: a case study of Chaersen Reservoir in Northeast China}
\author[1]{Weifei Yang}%
\author[1]{Changlai Xiao}%
\author[1]{Xiujuan Liang}%
\affil[1]{Jilin University}%
\vspace{-1em}
\date{\today}
\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
The number of reservoirs in the world is increasing year by year, which
will inevitably affect the downstream runoff and baseflow recession
characteristics. In this paper, the impact of Chaersen Reservoir
(Northeast China) on downstream runoff and baseflow recession
characteristics is studied using a pre post comparison method, and the
influence of climate change is excluded using two upstream
sub-watersheds as control basins. In addition, the impact mechanism of
the reservoir is further explored. The results show that the increased
direct and indirect water consumption after the construction of the
reservoir results in a 14\% reduction in the average streamflow of the
downstream Zhenxi Station. At the same time, the construction of the
reservoir causes the baseflow at the recession stage to increase by a
relatively constant value (about 1m\textsuperscript{3}/s), which leads
the log(\textbar{}dQ/dt\textbar{}) vs. log(Q) points at the low flow
stage to shift to the right. This eventually results in a decrease in
the recession coefficient a by about 60\% and an increase of b by about
24\%. The log(\textbar{}dQ/dt\textbar{}) vs. log(Q) scatter of the
recession process after adding a constant flow is no longer in a strict
linear relationship. And the master recession curve obtained by the
traditional linear parameterization method is only an average
approximation, which will overestimate the streamflow in the middle
recession stage and underestimate the streamflow in the late stage.%
\end{abstract}%
\sloppy
\section*{1 Introduction}
{\label{684855}}
There are about 16.7 million reservoirs larger than 0.01 ha in the
world, and this number will continue to increase (Lehner et al., 2011).
Currently, about half of the stream and river flow is regulated by
reservoirs and dams, and by 2030 this number will climb up to 90\%
(Grill et al., 2015). Reservoir's interception and regulation of river
flow will obviously weaken peak flow and increase low flow (Vorosmarty
et al., 1997). This will have a significant effect on the hydrological
process many hundred kilometers downstream of the dam (Grill et al.,
2015), as well as on the matter and energy cycle with streamflow as the
medium (Pringle, 2003). Qualitative or quantitative research on the
impact of reservoir construction on global or regional hydrological
process and ecological environment is of profound significance.
There are many researches about the impact of reservoir on the
hydrologic alteration and matter cycle: Grill et al. (2015) assessed the
patterns and trends in river fragmentation and flow regulation by global
dams at multiple scales; Hecht, Lacombe, Arias, Dang, and Piman (2019)
reviewed the hydrological impacts of the hydropower dams of Mekong River
basin; Taylor Maavara, D\selectlanguage{ngerman}ürr, and Van Cappellen (2014) studied the
worldwide retention of nutrient silicon by river damming; T. Maavara et
al. (2015) studied the global phosphorus retention by river damming; Van
Cappellen and Maavara (2016) studied the global scale modifications of
riverine nutrient fluxes by damming; Meanwhile, many researchers have
also studied the impact of reservoirs on downstream hydrological drought
(Rangecroft, Van Loon, Maureira, Verbist, \& Hannah, 2019; Tijdeman,
Hannaford, \& Stahl, 2018; Anne F. Van Loon et al., 2019; Wanders \&
Wada, 2015). In addition, reservoir's regulation of streamflow can also
influence the downstream baseflow recession characteristics. However, we
have not found some quantitative global or case studies, this study may
be the first attempt.
The shape of baseflow recession curve is an important feature of a
basin. Baseflow recession analysis can be understood as a statistical
analysis and description of the recession curve for a specific basin
(Stoelzle, Stahl, \& Weiler, 2013; Thomas, Vogel, \& Famiglietti, 2015).
The detailed baseflow recession analysis method will be described in
Section 3. Baseflow recession analysis is widely used in hydrological
research, water resources planning and management, and watershed
hydrogeological research (Sujono, Shikasho, \& Hiramatsu, 2004; Zhang,
Chen, Hickel, \& Shao, 2008). The main application objectives include:
estimating long-term groundwater storage trends (Brutsaert, 2008),
estimating watershed groundwater balance (H. Wittenberg \& Sivapalan,
1999), baseflow separation (Huyck, Pauwels, \& Verhoest, 2005; Hartmut
Wittenberg, 1999), baseflow regionalization (Beck et al., 2013), and
determining basin-wide hydrogeological parameters (Mendoza, Steenhuis,
Walter, \& Parlange, 2003; Oyarzún et al., 2014). The influence of
reservoir on the baseflow recession characteristics will obviously
affect the above application results, so we should first make clear the
influence mechanism of reservoir on the baseflow recession
characteristics.
At present, the main methods used to study the effects of human
activities on hydrological processes include scenario modelling method
(Veldkamp et al., 2015; Wada et al., 2017), paired catchments method
(Best, Zhang, McMahon, Western, \& Vertessy, 2003; Brown, Zhang,
McMahon, Western, \& Vertessy, 2005), pre\selectlanguage{english}-post comparison method (Liu et
al., 2016), upstream\selectlanguage{english}-downstream comparison method (Rangecroft et al.,
2019), and observation\selectlanguage{english}-modelling comparison method (A. F. Van Loon \&
Van Lanen, 2013). Anne F. Van Loon et al. (2019) has made a more
detailed summary. Among them, the simpler and more widely used method is
the pre\selectlanguage{english}-post comparison method, which selects two series before and
after human activities to compare. Penas, Barquin, and Alvarez (2016)
pointed out that this method is hard to separate human activities from
climate change, and it is usually necessary to select a control basin to
eliminate the impact of climate change. However, we must also be wary of
the spatial differences in climatic characteristics between the control
basin and the impact basin. These spatial differences may lead to
incorrect estimates of the impact of climate change.
The main purpose of this paper is to study the impact of Chaersen
reservoir (Northeast China) on the downstream runoff and baseflow
recession characteristics using the pre\selectlanguage{english}-post comparison method with two
upstream sub\selectlanguage{english}-watersheds as control basins. The main contents include: 1)
Before and after the construction of the reservoir, two streamflow
series with similar rainfall\selectlanguage{english}-runoff characteristics are selected to
compare the differences of runoff and baseflow recession
characteristics. 2) The impacts of climate change are estimated based on
two sub\selectlanguage{english}-basins in the upper reaches of the reservoir. 3) The influence
mechanism of the reservoir is further explored.
\section*{2 Study area and data}
{\label{study-area-and-data}}
The Tao'er River basin is located in northeastern China, with elevations
ranking from 100 m a.s.l. to 1600 m a.s.l. (Fig. 1). The drainage area
is 41,200 km\textsuperscript{2}, of which the bed rock mountain area
accounts for about 65\% and mainly distributed above the Zhenxi station
(Chen, Li, Li, \& Liu, 2016; Kou, 2016). The forest and grassland
coverage in the basin are about 25\% and 36\%, respectively, and the
rest is mostly cultivated with corn. This basin belongs to the temperate
continental monsoon climate zone, with an average annual rainfall of
463.6 mm (from 1953 to 2015).
The daily average precipitation, streamflow and water level of the four
hydrological stations in Tao'er River basin (Suolun, Dashizhai, Chaersen
and Zhenxi) have been monitored since 1964. The Zhenxi station is 63 km
away from the downstream of Chaersen reservoir, the Chaersen station is
located at the outlet of the reservoir, and the Suolun and Dashizhai
stations are located in the upstream watersheds (Fig. 1). The main
purpose of the paper is to analysis the influence of Chaersen reservoir
on the streamflow and baseflow recession characteristics of Zhenxi
station.
\textbf{Figure 1.} Location and topography of the Tao'er River basin.
The Chaersen Reservoir is a large-scale reservoir mainly used for flood
prevention, irrigation, combined power generation, and fish farming.
This reservoir started construction in 1987 and was completed in
September 1989. The drainage area it controls is 7872
km\textsuperscript{2}, the total design storage capacity is 11.48 x
10\textsuperscript{8}m\textsuperscript{3}, the design peak discharge is
420 m\textsuperscript{3}/s, and the design irrigation area is 590
km\textsuperscript{2}. The dam of the reservoir is a loam core wall dam
with a maximum height of 40 m, a crest width of 6 m and a length of 1712
m (Li, 2018). The streamflow regulation of Chaersen reservoir is usually
carried out from May to October every year. From 2009 to 2013, the
average daily discharge of the reservoir is between 20 to 110
m\textsuperscript{3}/s, with an average of 45 m\textsuperscript{3}/s
(Fig. S1). After the construction of the reservoir, the streamflow
process of Chaersen station is obviously flattened, and the flood peak
originally concentrated in July and August is evenly distributed to May
to October.
After excluding the construction period (1987-1989) and the initial
impoundment stage (1989-1995), based on the similarity of
rainfall\selectlanguage{english}-runoff characteristics, 1982-1986 was determined as the
pre-disturbance period, and 2009-2013 was the post-disturbance period.
These two periods are the same length and the annual precipitation is
basically the same. Precipitation and runoff in both periods are all
increasing year by year (Fig. S2, Fig. S3). The unpublished streamflow
data was provided by Songliao Water Resources Commission, Ministry of
Water Resources, China.
\section*{3 Baseflow recession analysis
method}
{\label{baseflow-recession-analysis-method}}
The process of baseflow recession analysis can be divided into three
stages: 1) extracting the baseflow recession segment; 2) Selecting an
appropriate theoretical model; 3) Determining the optimal model
parameters (Tallaksen, 1995). Each stage has different methods. Stoelzle
et al. (2013) points out that different combinations of methods will get
different recession characteristics, and the parameter optimization
method has the most obvious effect on the results. Therefore, based on
the power function relationship of -dQ/dt and Q (Sect. 3.2), this study
adopts 12 combinations of four recession segment extraction methods
(Sect. 3.1) and three parameter optimization methods (Sect. 3.3) to
carry out the baseflow recession analysis. Finally, the difference of
mean value of recession coefficient under different parameter
optimization methods is analyzed. The Matlab toolbox developed by
Arciniega-Esparza, Brena-Naranjo, Pedrozo-Acuna, and Appendini (2017)
facilitates the automated analysis and comparative analysis of recession
analysis.
\subsection*{3.1 Baseflow recession segment extraction
methods}
{\label{baseflow-recession-segment-extraction-methods}}
There are four commonly used methods for extracting recession segments:
Kir method (Kirchner, 2009), Vog method (Vogel \& Kroll, 1992), Bru
method (Brutsaert \& Nieber, 1977) and Aks method (Aksoy \& Wittenberg,
2011). See Table 1 for a summary. The Vog method selects recession
segments from the decreasing parts of 3-day moving averages of
streamflow. Kir, Bru and Aks methods all select the recession segment in
the parts of \(dQ/dt\) \textless{} 0. Kir method includes all
the parts of \(dQ/dt\) \textless{} 0, while Bru and Aks methods
exclude the segments affected by rainfall or surface runoff based on
different criteria. Stoelzle et al. (2013) and Arciniega-Esparza et al.
(2017) has discussed these methods in detail, and this paper will not
repeat them.
\textbf{Table 1.} Recession segment extraction methods.
\subsection*{3.2 Theoretical model}
{\label{theoretical-model}}
Hydrologists have provided a variety of theoretical models for baseflow
recession analysis based on different assumptions, see Tallaksen (1995),
Smakhtin (2001), and Thomas et al. (2015) for details. The current
widely used theoretical models are: linear reservoir model,
\(S=kQ\) (Maillet, 1905), nonlinear reservoir
model,\(\ S=kQ^{\beta}\) (Hartmut Wittenberg, 1999) and power law
relationship between \(-dQ/dt\) and Q,\(-\frac{\text{dQ}}{\text{dt}}=aQ^{b}\)
(Brutsaert \& Nieber, 1977). These three models are essentially based on
the power law relationship between storage and discharge (also named
nonlinear reservoir). The linear reservoir model presents a particular
feature as it has a power exponent of 1. The power function relationship
of \(-dQ/dt\) and Q can be obtained by substituting the power
law relationship between storage and discharge into the basin continuity
equation(Wang \& Cai, 2009),\emph{k} and \selectlanguage{greek}\emph{β} \selectlanguage{english}are constants and the
corresponding relationships with \emph{a} and \emph{b} are
\(k=\frac{1}{\left(2a-ab\right)}\)and \(\beta=2-b\) (Thomas et al., 2015).
Therefore, this study uses the power function model of
\(-dQ/dt\) and Q to analyze the recession. It should be pointed
out that the water storage (S) here refers to the active water storage
in the basin that can be discharged to the river.
\subsection*{3.3 Parameter optimization
methods}
{\label{parameter-optimization-methods}}
There are three widely used parameter optimization methods: linear
regression, lower envelope, and binning (Stoelzle et al., 2013).
According to the scatter plot of log(\textbar{}d\emph{Q} /d\emph{t}
\textbar{}) vs. log(\emph{Q} ), the three parameter optimization methods
determine the fitting line with different forms. The slope of the line
is \emph{b} , and the intercept is log(\emph{a} ),\(\log\left(-\frac{\text{dQ}}{\text{dt}}\right)=\log\left(a\right)+b*\log(Q)\).
The linear regression method is to fit the line with the least square of
all scattered points. The lower envelope method uses the lower boundary
(or lower 5\% critical line) of the scatter points to determine the
fitting line. The binning method is to segment the scattered points
according to the streamflow, then calculate the average value of each
segment, and finally make the least square fitting line of these average
values. Stoelzle et al. (2013) and Thomas et al. (2015) have discussed
these methods in detail.
\section*{4 Results}
{\label{results}}
\subsection*{4.1 Impact of reservoir on
runoff}
{\label{impact-of-reservoir-on-runoff}}
Before the baseflow recession analysis, we first analyze the impact of
the construction of the Chaersen Reservoir on the runoff of the
downstream Zhenxi station. The streamflow duration curves of the two
comparison periods before and after the construction of the reservoir
are plotted, respectively, as shown in Fig. 2. After the construction of
the reservoir, the peak flow of Zhenxi station decreases significantly,
while the low-flow increases significantly (Fig. 2c). The streamflow of
the two upstream hydrological stations (Suolun and Dashizhai) are not
significantly different in the comparison periods (Fig. 2a, Fig. 2b),
and the deviations in some stages may be mainly caused by the
differences in climate characteristics. In order to quantitatively
analyze the impact of reservoir construction on runoff, we have
calculated some characteristic values of streamflow (maximum:
Q\textsubscript{max}, minimum: Q\textsubscript{min}, average:
Q\textsubscript{mean}, seven-days minimum: 7Q\textsubscript{min}) in the
comparison periods, and the percentage bias between them, as shown in
Table 2. The streamflow characteristic values of Suolun station in the
post-comparison period is generally smaller, with a bias of -16\% to
-24\%. However, these values of Dashizhai station in the post-comparison
period are generally larger, with a bias of 11\% to 60\%. This opposite
deviation may be caused by the spatial difference of climate
characteristics, which will cancel each other when converging to
downstream Zhenxi station. Considering the large difference between the
streamflow magnitude of the two upstream hydrological stations, this
study estimates the impact of climate change on the downstream Zhenxi
station by weighted average of the two opposite deviations, with the
proportion of average streamflow as the weight (Table 2). After
excluding the impact of climate change, the construction of Chaersen
Reservoir resulted in a 14\% decrease in Q\textsubscript{mean} of the
downstream Zhenxi station, a 35\% decrease in Q\textsubscript{max}, a
380\% increase in Q\textsubscript{min}, and a 235\% increase in
7Q\textsubscript{min}.
\textbf{Figure 2.} Streamflow duration curves before and after the
construction of reservoir. (a) Suolun station, (b) Dashizhai station,
(c) Zhenxi station.
\textbf{Table 2.} Characteristic values of streamflow before and after
the construction of reservoir, m\textsuperscript{3}/s.
\subsection*{4.2 Impact of reservoir on baseflow recession
characteristics}
{\label{impact-of-reservoir-on-baseflow-recession-characteristics}}
According to the method in section 3, the recession coefficients of the
two comparison periods are calculated, and the results are shown in Fig.
3. In general, after the construction of the reservoir, the recession
coefficient \emph{a} of the downstream Zhenxi station is obviously
reduced, while the coefficient \emph{b} is obviously increased. No
significant differences are found in the recession coefficients of the
comparison periods at the upstream Suolun and Dashizhai stations (Fig.
3). There are inherent differences in the coefficients calculated by
different methods, Stoelzle et al. (2013) has discussed it in detail.
The recession coefficients obtained by linear regression and binning
methods are not significantly different, but these obtained by the lower
envelope method are significantly lower. Different baseflow recession
segments extraction methods always cause certain fluctuations in the
coefficients. Therefore, we use the same recession analysis method
combination to avoid misjudgment due to the systematic error between
different recession analysis methods (e.g. using the average result of
different recession extraction methods under the same parameter
optimization method, Table 3).
In order to quantitatively analyze the impact of the reservoir, we
calculate the percentage bias between the average results of the two
comparison periods, as shown in Table 3. Under different parameter
optimization methods, the average recession coefficients \emph{a} in the
post-comparison period of Suolun station are smaller, with a bias of
-3\% to -10\%, and the average values of \emph{b} are larger, with a
bias of 0\% to 3\%. The average \emph{a} of Dashizhai station are also
smaller, with a bias of -12\% to -15\%, and the average \emph{b} are
slightly larger, with a bias of -2\% to 6\%. The deviation directions of
the two upstream hydrological stations are the same, so this study uses
the average of the two hydrological stations to estimate the bias caused
by the climate change between the comparison periods of the downstream
Zhenxi station (Table 3). After excluding the influence of climate
change, the recession coefficient \emph{a} of different parameter
optimization methods decreased by 57\% \textasciitilde{} 63\% (the
average is 60\%), and the \emph{b} increased by 21\% \textasciitilde{}
27\% (the average is 24\%). Although different parameter optimization
methods will get different results, the impact degree of reservoir
construction on the analysis results of different methods is basically
the same.
In order to more intuitively observe the influence of reservoir
construction on the baseflow recession characteristics, we have drawn
the master recession curves of the comparison periods before and after
the construction of the reservoir, the equation is\(\ Q_{(t+t)}=\left[a\left(b-1\right)t+Q_{t}^{1-b}\right]^{\frac{1}{1-b}}\), as
shown in Fig. 4, Fig. S4 and Fig. S5. When the recession starts with
average streamflow (Fig. 4a), the master recession curves of Zhenxi
station before and after the construction of the reservoir intersects.
And the master recession curve after the construction of the reservoir
is significantly higher in the later stage of the recession, and the
recession rate becomes significantly smaller. However, the master
recession curves of Suolun and Dashizhai stations do not change
significantly before and after the construction of the reservoir. When
the recession starts with same streamflow (Fig. 4b), the master
recession curves of Zhenxi, Suolun and Dashizhai stations before the
construction of the reservoir are basically the same. However, after the
construction of the reservoir, the master recession curve of Zhenxi
station is significantly higher. In other words, the construction of the
reservoir slows the baseflow recession rate and increases the drainage
time, causing the master recession curve to shift upward.
Changes in the recession coefficients and the master recession curve
also mean changes in the basin-scale storage-discharge (S-Q)
relationship. Based on the nonlinear reservoir theoretical model (Sect.
3.2), the S-Q relationship curves before and after the construction of
the reservoir are drawn, as shown in Fig. 5, Fig. S6, and Fig. S7.
Before the construction of the reservoir, the S-Q relationships of the
three hydrological stations are approximately linear, but the
nonlinearity of the S-Q relationship of Zhenxi station increases
significantly after the construction of the reservoir. In addition,
after the construction of the reservoir, the storage capacity of Zhenxi
station increases significantly. When the baseflow is
5m\textsuperscript{3}/s (60\textsuperscript{th} percentile), the storage
capacity is 3.1 times before the reservoir construction; When the
baseflow is 20m\textsuperscript{3}/s (32\textsuperscript{th}percentile),
the storage capacity becomes 2.1 times as before; When the baseflow is
40m\textsuperscript{3}/s (22\textsuperscript{th}percentile), the storage
capacity becomes 1.7 times as before. That is, with the increase of
baseflow, the increase rate of basin-scale storage becomes slower and
slower.
\textbf{Figure 3.} Recession coefficients analysis results of different
methods. LR: linear regression, BIN: binning, LE: lower envelope.
\textbf{Table 3.} The average recession coefficients of different
parameter optimization methods. The bias is calculated with
1982\textasciitilde{}1986 as reference.
\textbf{Figure 4.} Master recession curves, with the recession
coefficients calculated by linear regression (LR) method. (a) Recession
starts with average streamflow, (b) recession starts with same
streamflow.
\textbf{Figure 5.} Storage-discharge (S-Q) relationship curves, with the
recession coefficients calculated by linear regression (LR) method.
\section*{5 Discussion}
{\label{discussion}}
\subsection*{5.1 Impact mechanism of
reservoir}
{\label{impact-mechanism-of-reservoir}}
One of the main functions of the reservoir is to regulate runoff to
reduce the influence of floods. Usually, a part of the reservoir
capacity is discharged before the wet season arrives, and some of the
peak flow is intercepted during the wet season. This regulation will
obviously reduce flood peaks and increase baseflow during dry season,
but will not directly affect the total runoff. In this study, the
average streamflow of the downstream Zhenxi station decreased by about
14\% after the construction of the reservoir, which may be mainly due to
the increased direct consumption of water surface evaporation and
indirect consumption of drinking water, farmland irrigation, etc. (T.
Maavara, Lauerwald, Regnier, \& Van Cappellen, 2017; Vorosmarty et al.,
1997). In other words, the regulation of runoff by the reservoir
directly leads to the reduction of peak flow and the increase of
baseflow, and the increased direct and indirect consumption of reservoir
storage leads to the reduction of average or total streamflow. Some
studies have well illustrated the above point of view: Piman, Lennaerts,
and Southalack (2013) showed that hydropower dams in the tributaries of
the Mekong River will lead to a 25\% decrease in the streamflow during
the wet-season and an increase of 95\% during the dry-season; In
addition, Piman, Cochrane, and Arias (2016) also pointed out that the
impact of hydropower dams on the downstream daily average streamflow is
greater, with the maximum daily streamflow decreasing by 36\% and the
minimum increasing by 168\%. Vorosmarty et al. (1997) pointed out that
the loss of Lake Nasser on the Nile River caused by evaporation and
leakage accounts for about 13\% of its inflow every year, Lake Kariba on
the Zambezi River loses about 20\% of its inflow, while the smaller Tiga
Reservoir loses about 26\% of its upstream inflow. Shiklomanov (2000)
pointed out that the reservoir-based evaporation would increase the
consumption of the global river runoff by about 5\%, which would be
larger if the domestic, industrial and agricultural water consumption
were taken into account.
As mentioned in Section 4.2, after the construction of the reservoir,
the recession coefficient \emph{a} of downstream Zhenxi station is
significantly reduced, while \emph{b} is significantly increased, which
leads to the change of the master recession curve and S-Q relationship.
Why is there such a change? We try to find the answer from the scatter
plot of log (\textbar{}d\emph{Q} /d\emph{t} \textbar{}) vs. log
(\emph{Q} ). By comparing the log (\textbar{}d\emph{Q} /d\emph{t}
\textbar{}) vs. log (\emph{Q} ) points distribution characteristics
before and after the construction of the reservoir at Zhenxi station, we
find that the scatter points at the low flow stage after the
construction of the reservoir are significantly shifted to the right
(Fig. 6a). This also results in a counterclockwise rotation of the
linear regression line, which eventually increases the slope (\emph{b} )
and decreases the intercept (\emph{a} ). This shift indicates that after
the construction of the reservoir, the downstream baseflow (\emph{Q} )
increased during the dry-season, but the recession rate (dQ/dt) did not
change significantly, which can be understood as the addition of a
constant flow (\emph{Q\textsubscript{con}} ) in the original recession
process. To verify this assumption, we add a constant value
(1m\textsuperscript{3}/s) to the master recession curve (MRC) of Zhenxi
station before the construction of the reservoir, and then draw the log
(\textbar{}d\emph{Q} /d\emph{t} \textbar{}) vs. log (\emph{Q} ) scatter
plots (Fig. 6b). It is found that the scatter points in the low flow
stage are also shifted to the right after increasing the constant value
of 1m\textsuperscript{3}/s to the MRC. The recession coefficient\emph{a}
calculated by the linear regression method is reduced to 0.01, and
\emph{b} is increased to 1.22, which are basically the same as the
calculated values after the construction of the reservoir. In addition,
it can be seen from Table 2 that after the construction of the
reservoir, the \emph{Q\textsubscript{min}} of Zhenxi station increased
from 0.29m\textsuperscript{3}/s to 1.38m\textsuperscript{3}/s, increased
by 1.09m\textsuperscript{3}/s, and the 7Q\textsubscript{min} increased
by 7.03m\textsuperscript{3}/s, which should not be a coincidence. That
is to say, after the construction of the reservoir, the variable water
storage of the basin is increased, and a relatively constant flow (about
1m\textsuperscript{3}/s) is added to the baseflow recession process of
downstream Zhenxi station, which causes the log (\textbar{}d\emph{Q}
/d\emph{t} \textbar{}) vs. log (\emph{Q} ) scatter points to shift to
the right in the late recession stage, and finally causes the recession
coefficient \emph{a} to decrease and \emph{b} to increase. This
relatively constant flow may come from the continuous and stable
drainage of the reservoir, the continuous leakage, and the continuous
return flow of domestic, agricultural and industrial used reservoir
water. Specific sources still require detailed field investigations.
\textbf{Figure 6.} -dQ/dt vs. Q scatter plots in double logarithmic
coordinate system, with the Vog extraction method. The lines in the
figure are linear regression lines. (a) Observed values before and after
the construction of the reservoir, (b) calculated values before and
after adding a constant flow.
\subsection*{5.2 Master recession analysis considering constant addition
flow}
{\label{master-recession-analysis-considering-constant-addition-flow}}
It can be seen from Fig. 6b that after adding a constant flow, the log
(\textbar{}d\emph{Q} /d\emph{t} \textbar{}) vs. log (\emph{Q} ) scatter
points no longer satisfies the linear relationship, and the slope
gradually becomes larger in the late stage of the recession. The
recession coefficients calculated by the traditional linear
parameterization method reflect the average linear approximation of this
nonlinear relationship. Therefore, a new master recession analysis
method should be adopted to analyze the baseflow recession process with
a constant addition flow. For example, we can deduct the constant flow
(\emph{Q\textsubscript{con}} ) before drawing the log
(\textbar{}d\emph{Q} /d\emph{t} \textbar{}) vs. log (\emph{Q} ) scatter
plot for parameterization (i.e. all measured recession streamflow
minus\emph{Q\textsubscript{con}} ), and then add
\emph{Q\textsubscript{con}}to the master recession curve (MRC). At this
time, the equation of the MRC becomes\(Q_{(t+t)}=Q_{\text{con}}+\left[a\left(b-1\right)t+{(Q_{t}-Q_{\text{con}})}^{1-b}\right]^{\frac{1}{1-b}}\), where \emph{a}
and \emph{b} in the formula are obtained by linear regression of the
scattered points after subtracting\emph{Q\textsubscript{con}} . After
subtracting\emph{Q\textsubscript{con}} = 1m\textsuperscript{3}/s from
the recession streamflow of Zhenxi station from 2009 to 2013, the
recession coefficients \emph{a} and \emph{b} are 0.022 and 1.10
respectively, which are close to the analysis results of 1982 to 1986.
Finally, the MRC considering constant addition flow is drawn (Fig. 7).
After comparison, it can be found that the MRC obtained by the average
linear approximation will overestimate the streamflow in the middle
recession and underestimate the streamflow in the later recession, but
the deviation is not obvious as a whole. That is to say, in Sect. 4.2,
the recession analysis results obtained by linear approximation can
reflect the overall change of the recession characteristics before and
after the construction of the reservoir, but there will be some
deviations in some specific stages. The master recession analysis method
proposed in this paper can also be used in basins with inter-basin water
transfer behavior. When there is stable inflow,
\emph{Q\textsubscript{con}} is positive, and when there is stable
outflow, \emph{Q\textsubscript{con}}is negative.
\textbf{Figure 7.} Master recession curves with and without constant
addition flow, semi logarithmic coordinate.
\section*{6 Conclusions}
{\label{conclusions}}
In this paper, the impact of Chaersen Reservoir (Northeast China) on
downstream runoff and baseflow recession characteristics is studied
using a pre-post comparison method, and the influence of climate change
is excluded using two upstream sub-watersheds as control basins. In
addition, the impact mechanism of the reservoir is further explored. The
main conclusions are as follows:
1) After the construction of Chaersen Reservoir, the regulation of
runoff directly leads to the reduction of peak flow
(\emph{Q\textsubscript{max}} decreased by 35\%) and the increase of
baseflow (\emph{7Q\textsubscript{min}} increased by 235\%) of downstream
Zhenxi station, and the increased direct and indirect consumption of
reservoir storage leads to the reduction of average or total streamflow
by about 14\%.
2) After the construction of the reservoir, a relatively constant flow
(about 1m\textsuperscript{3}/s) is added to the baseflow recession
process of downstream Zhenxi station, which causes the log
(\textbar{}d\emph{Q} /d\emph{t} \textbar{}) vs. log (\emph{Q} ) scatter
points to shift to the right in the late recession stage, and finally
causes the recession coefficient \emph{a} to decrease by about 60\%
and\emph{b} to increase by about 24\%. This change also means the upward
shift of the MRC, the slowing down of the recession rate, and the
increase of the active water storage in the basin.
3) After adding a constant flow, the log (\textbar{}d\emph{Q} /d\emph{t}
\textbar{}) vs. log (\emph{Q} ) scatter points no longer satisfies a
strict linear relationship. The MRC obtained by the traditional linear
parameterization method is only an average approximation, which will
overestimate the streamflow in the middle recession stage and
underestimate the streamflow in the later stage, but the deviation is
not obvious as a whole.
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\section*{Tables}
{\label{tables}}
\textbf{Table 1.} Recession segment extraction methods.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Extraction methods} & \textbf{Criterion} & \textbf{Minimum
recession length (days)} & \textbf{Excluding exterior parts of recession
segment} & \textbf{Exclusion of anomalous recession
decline}\tabularnewline
\midrule
\endhead
Kir & \(dQ/dt<0\) & 1 & -- & --\tabularnewline
Vog & Decreasing 3-d moving average & 10 & First 30\% &
\(\frac{\left(Q_{i}-Q_{i+1}\right)}{Q_{I+1}}>30\%\)\tabularnewline
Bru & \(dQ/dt<0\) & 6--7 & First 3---4, last 2 &
\(\frac{dQ_{t+1}}{\text{dt}}>\frac{dQ_{t}}{\text{dt}}\)\tabularnewline
Aks & \(dQ/dt<0\) & 5 & First 2 & CV \textgreater{}
0.20\tabularnewline
\bottomrule
\end{longtable}
\textbf{Table 2.} Characteristic values of streamflow before and after
the construction of reservoir, m\textsuperscript{3}/s.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Hydrological station} & \textbf{Record period} &
\textbf{Q\textsubscript{max}} & \textbf{Q\textsubscript{min}} &
\textbf{Q\textsubscript{mean}} &
\textbf{7Q\textsubscript{min}}\tabularnewline
\midrule
\endhead
Suolun & 1982\textasciitilde{}1986 & 524.00 & 0.29 & 24.69 &
2.03\tabularnewline
& 2009\textasciitilde{}2013 & 396.00 & 0.24 & 19.54 &
1.71\tabularnewline
& Bias & -24\% & -18\% & -21\% & -16\%\tabularnewline
Dashiazhai & 1982\textasciitilde{}1986 & 152.00 & 0.06 & 9.45 &
0.36\tabularnewline
& 2009\textasciitilde{}2013 & 170.00 & 0.08 & 10.47 &
0.58\tabularnewline
& Bias & 12\% & 31\% & 11\% & 60\%\tabularnewline
Zhenxi & 1982\textasciitilde{}1986 & 847.00 & 0.29 & 40.07 &
2.92\tabularnewline
& 2009\textasciitilde{}2013 & 430.00 & 1.38 & 29.73 &
9.94\tabularnewline
& Bias & -49\% & 376\% & -26\% & 240\%\tabularnewline
& Bias (climate change) \textsuperscript{a} & -14\% & -4\% & -12\% &
5\%\tabularnewline
& Bias (ex-climate change) & -35\% & 380\% & -14\% &
235\%\tabularnewline
\bottomrule
\end{longtable}
\textsuperscript{a} The weighted average calculation formula is: Bias
(climate change) = Bias (Suolun)*0.72 + Bias (Dashizhai)*0.28.
\textbf{Table 3.} The average recession coefficients of different
parameter optimization methods. The bias is calculated with
1982\textasciitilde{}1986 as reference.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\textbf{Hydrological station} & \textbf{Record period} & \textbf{a} &
\textbf{a} & \textbf{a} & \textbf{b} & \textbf{b} &
\textbf{b}\tabularnewline
\midrule
\endhead
& & \textbf{LR} & \textbf{BIN} & \textbf{LE} & \textbf{LR} &
\textbf{BIN} & \textbf{LE}\tabularnewline
Suolun & 1982\textasciitilde{}1986 & 0.0317 & 0.0317 & 0.0095 & 1.11 &
1.11 & 1.04\tabularnewline
& 2009\textasciitilde{}2013 & 0.0303 & 0.0307 & 0.0086 & 1.14 & 1.14 &
1.04\tabularnewline
& Bias & -5\% & -3\% & -10\% & 3\% & 2\% & 0\%\tabularnewline
Dashizhai & 1982\textasciitilde{}1986 & 0.0446 & 0.0473 & 0.0119 & 1.07
& 1.00 & 0.95\tabularnewline
& 2009\textasciitilde{}2013 & 0.0394 & 0.0400 & 0.0102 & 1.05 & 1.04 &
1.01\tabularnewline
& Bias & -12\% & -15\% & -14\% & -2\% & 4\% & 6\%\tabularnewline
Zhenxi & 1982\textasciitilde{}1986 & 0.0363 & 0.0352 & 0.0084 & 1.02 &
1.03 & 0.94\tabularnewline
& 2009\textasciitilde{}2013 & 0.0104 & 0.0104 & 0.0026 & 1.31 & 1.31 &
1.18\tabularnewline
& Bias & -71\% & -71\% & -69\% & 28\% & 27\% & 25\%\tabularnewline
& Bias (climate change) \textsuperscript{a} & -8\% & -9\% & -12\% & 1\%
& 3\% & 3\%\tabularnewline
& Bias (ex-climate change) & -63\% & -61\% & -57\% & 27\% & 23\% &
21\%\tabularnewline
\bottomrule
\end{longtable}
\textsuperscript{a} Estimated based on the average bias of the Suolun
and Dashizhai stations.
\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure/Figure}
\caption{{\textbf{Figure 1.} Location and topography of the Tao'er River basin.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figurf/Figurf}
\caption{{\textbf{Figure 2.} Streamflow duration curves before and after the
construction of reservoir. (a) Suolun station, (b) Dashizhai station,
(c) Zhenxi station.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figurg/Figurg}
\caption{{\textbf{Figure 3.} Recession coefficients analysis results of different
methods. LR: linear regression, BIN: binning, LE: lower envelope.%
}}
\end{center}
\end{figure}
\textbf{}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figurh/Figurh}
\caption{{\textbf{Figure 4.} Master recession curves, with the recession
coefficients calculated by linear regression (LR) method. (a) Recession
starts with average streamflow, (b) recession starts with same
streamflow.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figuri/Figuri}
\caption{{\textbf{Figure 5.} Storage-discharge (S-Q) relationship curves, with the
recession coefficients calculated by linear regression (LR) method.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figurj/Figurj}
\caption{{\textbf{Figure 6.} -dQ/dt vs. Q scatter plots in double logarithmic
coordinate system, with the Vog extraction method. The lines in the
figure are linear regression lines. (a) Observed values before and after
the construction of the reservoir, (b) calculated values before and
after adding a constant flow.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figurk/Figurk}
\caption{{\textbf{Figure 7.} Master recession curves with and without constant
addition flow, semi logarithmic coordinate.%
}}
\end{center}
\end{figure}
\selectlanguage{english}
\FloatBarrier
\end{document}