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Quantitative identification of natural flow regimes in fish spawning seasons
Ecological Engineering 138 (2019) 209–218
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Ecological Engineering journal homepage: www.elsevier.com/locate/ecoleng
Quantitative identification of natural flow regimes in fish spawning seasons a
a
Fang-Fang Li , Cong-Min Liu , Jun Qiu a b
b,⁎
T
College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China State Key Laboratory of Hydroscience & Engineering, Tsinghua University, Beijing 100084, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Flow regimes Quantitative identification Characteristic parameter Fish spawning Yellow River
The construction and operation of water conservancy projects changes the original flow regimes of rivers, and thus disturbs the habitat conditions of aquatic organisms. As an important aquatic living resource, fish is sensitive to the flow changes of the river, especially in the important periods such as spawning seasons. Existing studies indicates that flow regimes are effective stimulus for the fish spawning. In this study, an identification algorithm composed of three times of denoising is proposed to identify those rises with a certain magnitude and duration, which are deemed to be effective for spawning stimulus. Characteristic parameters including the number of the flood processes in the spawning season, the duration of each flood, and the growth rate of rising are statistically analyzed for the identified processes. The application on the upper reaches of the Yellow River in China, where almost no human intervention occurs, indicates that there indeed are some regularity for the flow regimes in the spawning season, even in different hydrological years. 4–6 times of floods composing a flow rising process is in need, and each of them should last for about 11 days. The desired daily growth rate is around 60 m3/(s·d) for the studied case. Such results are conductive for reservoir operation and the management of the river.
1. Introduction Natural river flow with cyclical fluctuations provides a diverse and suitable habitat for aquatic community to sustain multiple species. The flow variability plays an especially important role in different life stages of a specific species (Chang et al., 2011). The construction and operation of water conservancy projects attenuates such fluctuation, even breaks the flow, resulting in the reduction of the diversity and richness of aquatic organisms (Gao, 2006; Hardie, 2013; Coops and Hosper, 2002; Larson et al., 2016). Although replicating natural flow regimes in managed systems can be impossible, incorporating elements of the natural flow regime (like ascending baseflows) can benefit the restoration and conservation of riverine species (Goodman et al., 2018). Fish are important aquatic living resources with high trophic levels in aquatic ecosystems, and it can be a reliable indicator of the ecological balance in stream (Hur et al., 2017; Li et al., 2011; Liu and Men, 2007; Misetic et al., 2003; Suen et al., 2009; Wu et al., 2014; Zhao et al., 2017). Changes of hydrological and hydraulic factors related to river flow, i.e., flow, flow rate, water quality and hydrological conditions, affect the physiological activities of fish (Poff and Zimmerman, 2010; Shen et al., 2018). Flow regimes contain not only the flow magnitude but also other parameters such as time, the duration of the flow rise, growth rate, and ⁎
the number of flow peaks (Poff and Zimmerman, 2010), which can effectively stimulate fish for spawning (Alonso-Gonzalez et al., 2008; Chen et al., 2015; Suen and Herricks, 2009; Stanley et al., 1978; Rakowitz et al., 2008; Grabowski and Isely, 2007), and influence the spawning time and abundance of eggs and juveniles (Nagaya et al., 2008). In the study of Bailly et al. (2008), period, duration and intensity of floods were the considered flooding attributes, and they declared that intense floods favored gonad development and increased fish survival during initial development. Ozen and Noble (2002) collected Age0 largemouth bass Micropterus salmoides over seven years throughout the spawning season in Lucchetti Reservoir, Puerto Rico, and they concluded that the initiation of largemouth bass spawning was stimulated by water level increase. Zhang et al. (2015) believed that the fluctuations of water flow could effectively stimulate fish to spawn, and contributed to recovering river ecosystem diversity. Zhang et al. (2018) argued that an ecological flow regime with specific eco-hydrological signals (such as flow, frequency, duration, timing and rate of change) could not only guarantee oviposition but also meet the drifting conditions required for eggs. Agostinho et al. (2004) studied the influence of dam-controlled floods on some fish assemblage attributes, reproduction and recruitment in the Upper Parana River floodplain, and found that large floods were associated with higher species richness, more importantly, frequencies of individuals with ripe and partially spent
Corresponding author. E-mail address: [email protected] (J. Qiu).
https://doi.org/10.1016/j.ecoleng.2019.07.024 Received 11 December 2018; Received in revised form 15 March 2019; Accepted 23 July 2019 0925-8574/ © 2019 Elsevier B.V. All rights reserved.
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needs of fish community using an autecology matrix, which are directly related to the flow requirements of fish community. Sung et al. (2005) calculated ecological flow through habitat simulation, where the Habitat Suitability Criteria was developed for the two fish species (Zacco Platypus and Zacco Temmincki) and life stages (spawning and adult) and habitat conditions (depth, velocity and cover) need by fish. Various practical difficulties lie in studying fish-flow relationships from the perspective of fish demand for river flow. One of the major challenges is that the data on the demand of aquatic organisms for river flow is difficult to obtain in most cases due to the scarcity of data on aquatic ecosystems (Sun et al., 2012), and there is a lack of sufficient knowledge of the needs of fish communities. In addition, for any of the species, the specific physiological requirements for a particular element of the river flow are difficult to observe separately. Most of the previous studies have considered the relationship between the weighted available area of aquatic habitats or the quality of habitats and water flow (Boavida et al., 2018; Hsu et al., 2012; Johnson et al., 2017; Kim et al., 2016; Li et al., 2015), with little regard to the direct relationship between aquatic organisms and the flow regime. In addition, they only focused on individual fish species (e.g. Chinese sturgeon, Australian Grayling, Grass Carp). Long-term observations (more than 3 years) can hardly be found (Growns and James, 2005; King et al., 2015), which affects the universality of the results. The other perspective to study the flow demand for the fish spawning is from the flow regimes. Some publications have studied the characteristics of the hydrological statistics to determine the ecological flow (Chang et al., 2008; Chang et al., 2011; Tsai et al., 2015), which is defined as a human-modified flow regime that captures the natural flow variability for maintaining the structure and the functional integrity of the aquatic ecosystems by Suen (2011). Huang et al. (2014) proposed a flow frequency interval ratio method to calculate the ecological flow, and they divide the monthly flow series by 25% and 73% guarantee rate to 3 intervals including wet, normal and dry year, and the ecological flow of different levels is calculated in each interval. The environmental flows progressively reduce with the neglect of ecological protection, and they are relatively low for most rivers compared to environmental management objective (Smakhtin and Eriyagama, 2008). The environment required for the survival and reproduction of fish is formed through a long-term biological evolution, the relative stability of which is the prerequisite for the stability of fish diversity and abundance (Zou, 2011). Modern water resources management adopts natural flow regime as a target for river ecological restoration and protection (Chang et al., 2011; Gao, 2006; Suen, 2011; Xu et al., 2015). Sun et al. (2012) considered that the rivers were less disturbed by human activities in the 1950s, so the average daily variation of river flow during this period was taken as the target environmental flows. TGR has introduced manmade flood by ecological operation experiments to facilitate spawning of the four major Chinese carps. In this study, taking fish as the indicator of the health of aquatic ecosystems, the flow regimes in fish spawning season is identified by feature extraction on the natural flow process. Those floods with a certain fluctuation range are considered to be effective for spawning stimulus compared with small random fluctuations, which are identified by different ways of signal de-noising. The statistical characteristics of the effective floods are then drawn for different hydrological years. The methodology is applied on the upper reaches of the Yellow River, where almost no human intervention exists. The daily flow data from the year 2009 to 2016 in spawning seasons at Tangnaihe station, the very first hydrometric station of the Yellow River, are analyzed. The results indeed show some regularity features, i.e., there exist 4–6 times of floods composing a flow rising process is in need; each lasts for about 11 days, and the daily growth rate of the floods is about 60 m3/s. Such results verify the knowledge that there exist some quantitative flow regimes patterns in fish spawning seasons, which may be required by the fish spawning, and provide convictive references for river management in the future.
gonads, which indicate spawning, were higher during the period of increasing water level. Chehade et al. (2015) sought to verify experimentally which gonadal changes were present in mature individuals of Astyanax altiparanae arising from decreased water level, and found the water level drawdown applied to specimens in spawning capable phase of Astyanax altiparanae to induce reproduction was effective, especially in females. Seasonal flood peaks can be weakened or eliminated by reservoir scheduling, so that the triggering factors required for fish migration, spawning and hatching are interrupted, which is detrimental for fish reproduction (Leira and Cantonati, 2008). A study by Piana et al. (2017) found that intensity of floods determined the inter-annual variation in abundances of curimba Prochilodus lineatus, and Porto Primavera Dam negatively impacted the abundances at sites in the floodplain. Phelan et al. (2017) reported that reduced flow led to a decrease in fish abundance and diversity. Tan et al. (2010) found that flow changes of the Pearl River in China resulted in delays in fish spawning time and a decrease in fish larval abundance. Shen et al. (2018) stated that after the impoundment of the Three Gorges Reservoir (TGR), the natural river flow process changed, the habitat of the Chinese sturgeon was degraded, and its spawn time was delayed. Compared with other fish that produce drifting eggs, the four major Chinese carps (i.e. grass carp (Ctenopharyngodon idellus), black carp (Mylopharyngodon piceus), silver carp (Hypophthalmichthys molitrix), and bighead carp (Aristichys nobilis)) in the upper reaches of the Yangtze River have stricter requirements on the flow process during the breeding season. Shen (2015) conducted a statistical analysis of the relationship between the number of eggs and the index of flow rising in the Jiangjin section, and found that the scale of spawning is closely related to the process of flow rising. By correlation analysis and significant test, Shen (2015) proved that the initial flow and the daily average flow increase were the most critical indicators for the reproduction of the four major Chinese carps. The existing studies have demonstrated the importance of identifying the most suitable flow regimes for fish spawning for river management (Coops et al., 2004). There are two perspectives to study the fish demand for river flow. One starts from the fish. Koster et al. (2018) examined the influence of flow on spawning of the Australian grayling, using acoustic telemetry and drift sampling of eggs, they confirmed that egg concentrations peaked when weekly flows increased, and this information has been incorporated into the development of targeted environmental flows in the Bunyip-Tatago river system. In Girnock Burn, Aberdeenshire, Scotland, Webb et al. (2001) investigated 428 spawning events in 48 spawning sites and calculated the discharge, they found that female atlantic salmon need higher flow for spawning during the spawning season. Xu et al. (2015) investigated the four major Chinese carps spawning activities and their responses to the Three Gorges Reservoir (TGR) operation. Their statistical analysis showed that the flow effect on spawning could be expressed in three aspects (timing, amplitude, duration) by different hydrological variables: (i) the initial flood flow could affect the timing of spawning; the higher the initial flow level, the quicker the spawning response; (ii) the amplitude of flow increase could mostly affect the spawning scale (egg abundance); the higher the increase in the daily flow, the more abundant the eggs were in response; (iii) in annual variation, the spawning scale was positively correlated with continuous days of the rising hydrograph, but the relationship was very poor regarding the inter-annual data probably related to the different background in breeding populations. Taking the fish named Acrossocheilus paradoxus as the indicator species, Hsu et al. (2012) identified the most suitable value of ecological base flow of the TouQian River based on the adaptation indicators of its habitat by trade-off analysis and the incremental method. Due to the lack of natural hydrological statistics, Chang et al. (2011) used a point-biserial correlation method to relate fisheries collections with TEIS statistics, and then identified the ecological flow, which verifies the importance of fish as an indicator species on the study of ecological flow patterns. Suen and Herricks (2009) identified ecohydrological indicators by analyzing the 210
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1
Ki =
2.1. Identification of flood processes The flood process in this study refer to a complete wave with a certain amplitude and duration of fluctuation, and can be an effective stimulus for fish spawning. Simple connection of extreme points of the original sequence does not work due to the existence of a large amount of noises, as illustrated in Fig. 1. Therefore, smoothing the original time series to eliminate noises is the prerequisite for identifying flood process and extracting characteristic parameters.
y '= −y + a
i=n
(3)
(4)
2.1.1.4. Preliminary identification of the flood process. The peaks are firstly located and then the two troughs on the left and right sides of it are identified to form a complete flood process with “left trough – peak – right trough”. Such identification mechanism leads to the possibility of “trough – peak – … – peak – trough”, as shown in Fig. 4(a). The processing approach to avoid two peaks in the same flood process is that the one with a smaller value of the two peaks is eliminated after comparison of the two peaks. Hence the effective flood process with “left trough – peak – right trough” can be ensured, as shown in Fig. 4(b). It needs to be explained that there is no such thing as “trough – trough – … – trough – peak – trough – … – trough” due to the identification mechanism. The invalid troughs on the outsides will be automatically ignored, and will not affect the identification of the effective flood process.
(1)
Moving average processing takes the mean of the first derivatives at m successive points as the first derivative of the intermediate point. There are two ways to process the first (m − 1) and the last (m − 1) points, 2
as shown in Eq. (2) and Eq. (3), respectively. When (m − 1) is relatively 2 small, the first derivatives of the starting and end points is set to be 0, and the overall formula is shown in Eq. (2); while when (m − 1) is large, 2 smoothing is performed as much as possible, as shown in Eq. (3). j
⎧ 1 ∑ k , i = j + 1, j + 2, ⋯, n−j i+g m ⎨ g =−j ⎪ 0, i = 1, ⋯j, n − j + 1, ⋯n ⎩
i=1
2.1.1.3. Endpoint processing. Since the troughs and peaks are identified separately, the first or the last identified peaks may be eliminated as there is no troughs identified before or after the first or last peaks. In these cases, the starting points or the endpoint of the sequence are added to form a flood process, as illustrated in Fig. 2. Another possibility is that the flow falling process has not begun yet at the end of the defined spawning period, but the flow rising process continues and the maximal value exceeds the peak of the previous flood process. It can be regarded as an effective flow rising process stimulating fish to spawn. Thus the end point is defined as a peak in this case, and the duration of flood process is the time from the trough to the end, i.e., it can be considered that the end point is both a peak and a trough to form a complete and effective flow rising process, as shown in Fig. 3.
2.1.1. Preliminary denoising 2.1.1.1. Initial smoothing. It is difficult to applying curve fitting on the flow sequence without the knowledge of the distribution of the data on a flow process. Besides, the possibility of the flow sequences in different years obeying the same distribution is rather small. In this study, the moving average method is used to smooth the first derivative of flow sequence at first to eliminate those jagged noises with small fluctuations. The reason why the original flow sequence is not directly smoothed is that the moving average processing would modify the original information, and the identified peak and valley value would significantly differs from the original data. The first derivatives of the flow sequence at the i-th point ki is defined in Eq. (1)
Ki =
k1 + k2 , 2 kn − 1 + kn , 2
2.1.1.2. Preliminary identification of the troughs and peaks. A complete flood process includes the rising margin and the falling margin on the curve, which is described as “left trough – peak – right trough”. Thus the location of the troughs and the crests needs to be determined first. The minimums can be easily found with the help of the first derivative. However, due to the initial average smoothing, there exists an offset. Hence, after the initial smoothing which helps determine the location of the extremes within an offset of m, the trough is sequentially detected in the m adjacent points, i.e., the minimum of the m points oriented by the initial smoothing is set as the preliminary troughs. The determination of the peaks is similar, taking the opposite number of the original data and shifting the sequence upwards to avoid negative values, as in Eq. (4).
2. Methodology
2
⎨ ⎪ ⎪ ⎩
where Ki and ki indicate the first derivative at the i-th point before and after the averaging, respectively; j = (m − 1) . 2 The random variation of the first derivative {Ki} is reduced by the moving average processing, which is called initial smoothing in this study. It should be illustrated that such smoothing process can be repeated until the smoothing effect is satisfying.
Fig. 1. An example of original daily flow process in spawning season; Those fluctuations with a certain amplitudes is considered to be effective for fish spawning stimulus, as indicated by the black arrows; while those small fluctuations are regarded as noises, as indicated by the red arrows. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
y2 − y1 , i = 1; ⎧ ⎪ yi + 1 − yi − 1 , i = 2, 3, ⋯, n − 1; ki = 2 ⎨ ⎪ y − yn − 1 , i = n n ⎩
j
j + 2, ⋯, n − j ⎧ m ∑g =−j ki + g , i = j + 1, ⎪ 1 2i − 1 ⎪ 2i − 1 ∑r = 1 kr , i = 2, ⋯j, n − j + 1, ⋯n − 1
2.1.2. Double denoising Although the preliminary denoising is able to effectively remove the jagged noises, there still may exist slight fluctuations in the preliminary identification results. Only those waves with a certain amplitude of variation is considered to be effective stimulus for fish spawning. Analyzing the identification results of flood processes over the years, the difference between a peak and the left trough in a “left trough –
(2)
211
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(a) Before modification
(b) After modification
Fig. 2. Schematic diagram of the modification of the endpoints in consideration of the identification mechanism of the peaks and troughs, where the curve represents the water flow process, the triangle represents the peak, the asterisk represents the trough, and the adjacent “trough – peak – trough” represents a flood process. Before the modification, the flood at the end cannot be recognized, as in the red rectangle in (a); while after the modification, they are identified as indicated in (b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
peak – right trough” process is defined as the increment of a rising process. When the increment of a fluctuation is less than a certain percentage (which is set as 20% in this study case) of the maximum increment in the same year, it is regarded to be a small ineffective fluctuation and is eliminated in this study, as shown in Fig. 5. Such processing is called double denoising.
beginning of the rising process identified by smoothing, which is mistakenly classified as the rising margin. Such smooth processes are eliminated in this study by checking the slope of rising margin, and only those margin with the slope large enough and continuous is recognized as an effective flood process, as shown in Eq. (7). Such modification considering the slopes are illustrated in Fig. 7.
1 j
2.1.3. Modification of the troughs considering adjacent processes All the processing above directs at independent peaks and troughs without consideration of the relationship between adjacent troughs and peaks. If there are flow fluctuations on the rising margin and the decrement of the previous flood process is greater than a1 times the value of the latter increment as in Eq. (5), it is considered that the rising margin starts from the end of the previous falling margin, as shown in the dash rectangle in Fig. 6; while on the falling margin of a process, if there are flow fluctuations and the value of the latter increment is greater than a2 times the value of the previous decrement as in Eq. (6), it is considered that the falling margin ends after the previous falling, as shown in the dot rectangle in Fig. 6.
Qi > a1 × Qj
(5)
Qn > a2 × Qm
(6)
j
∑ Ki < a3 × i=1
1 n−j
n
∑
Ki
i=j+1
(7)
where n indicate the number of points of the rising margin; j-th point is the boundary between two slopes; Ki indicate the first derivative at the i-th point after moving averaging. 2.1.4. Triple denoising After the modification considering adjacent processes and slopes, another smoothing similar to that illustrated in Section 2.1.2 is taken to remove the small fluctuations, called triple denoising. 2.2. Characteristics of the flood processes In order to quantitatively describe the flow rising process and to provide quantitative reference for ecological scheduling of the reservoir, the following characteristic parameters are defined and
In addition, there may exists a rather smooth process at the
(a) Before processing
(b) After processing
Fig. 3. Schematic diagram of the modification of the end flood process in consideration of the actual water flow process, where the curve represents the water flow process, the triangle represents the peak, the asterisk represents the trough, and the adjacent “trough – peak – trough” represents a flood process. Before the modification, if the flow process ends with a rising edge, it would not be recognized as a part of effective flood as in the red rectangle in (a); while after the modification, it is identified as an effective flood, as indicated in (b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 212
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(a) Before processing
(b) After processing
Fig. 4. Schematic diagram of the modification of the “trough – peak – … – peak – trough” in consideration of the flood process, where the curve represents the water flow process, the triangle represents the peak, the asterisk represents the trough, and the adjacent “ trough – peak – trough” represents a flood process. Before the modification, the situation that multiple local maxima existing in the same flood may occur, as indicated in the rectangle in (a); while after the modification, only the highest one is reserved as the peak in a flood process, as indicated in (b).
(a) Before the double denoising
(b) After the double denoising
Fig. 5. Schematic diagram of the double denoising considering small fluctuations interference, where the curve represents the water flow process, the triangle represents the peak, the asterisk represents the trough, and the adjacent “ trough – peak – trough” represents a flood process. Before the second denoising, some small fluctuations may still exist in the identified flood process, as in the red rectangle in (a); while after the double denoising, they are omitted, as indicated in (b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
It is used to describe the mean durations of multiple flood processes in the spawning season of a certain year, as defined in Eq. (8):
analyzed: (1) The number of the flood process in the whole spawning season N ; − (2) The average duration in days of each flood process T :
−
N
T = (∑
i=1
(a) Before modification
Ti )/ N
(8)
(b) After modification
Fig. 6. Schematic diagram of the modification of the troughs position in consideration of the water flow trend of rising and fallinging, where the curve represents the water flow process, the triangle represents the peak, the asterisk represents the trough, and the adjacent “ trough – peak – trough” represents a flood process. After the modification, those fluctuations with a relatively large amplitude are excluded in a flood process. 213
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(a) Before modification
(b) After modification
Fig. 7. Schematic diagram of the modification of the troughs position in consideration of the slopes of the flow rising or falling edge, where the curve represents the water flow process, the triangle represents the peak, the asterisk represents the trough, and the adjacent “trough – peak – trough” represents a flood process. After the modification, if the slope of the rising or falling edge is relatively slow, it will be excluded in the flood process, as indicated in the red rectangle in (a) and (b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
(3) Average growth rate:
Start
It is used to describe the mean growth rate of multiple floods in a certain spawning season, which reflects both the increment and the temporal effect, as defined in Eq. (9): −
N
η = (∑
i=1
max(Qi ) − min(Qi ) )/ N Day|max(Qi ) −Day|min(Qi ) + 1
Initial smoothing
Original flow sequence
Preliminary identification of throughs and peaks
Preliminary denoising
Endpoint processing
(9)
Double denoising
−
The larger the η is, the severer a flood perceived by the fish is. Thus η is deemed to be an important parameter for fish spawning. To further analyze the stimulus of the flood to fish spawning in different hydrological years, the characteristic parameters defined above are statistically calculated in wet years, normal years, and dry years, respectively. −
Modification of troughs considering adjacent processes
Preliminary identification of flow rising processing
Triple denoising
2.3. Implementation
Flow rising processes
Both literatures and actual observations demonstrated that the continuous flood processes are the effective stimulus for fish spawning instead of small fluctuations, which is also the precondition and motivation of this study. Thus denoising is firstly operated on the first derivative of the natural flow process with moving average method to preserve the original information, known as “initial smoothing” in this study. The peaks and troughs of the smoothed process are determined to form completed “left trough – peak – right trough” flood regimes with the modification of the endpoints. To eliminate the survival small fluctuations which is invalid for spawning stimulus, a certain amplitude of the fluctuation is defined as the threshold for the secondary denosing, called “double smoothing”, by which those fluctuations with a range smaller than the threshold is omitted. Troughs are then modified again considering the relationships between adjacent processes to ensure the continuous rising and falling in one complete flood process. The identified flood processes are obtained after denoising for the third time, the “triple smoothing”, whose characteristics are quantified by the proposed parameters. The flow chart of the methodology proposed in this study is illustrated in Fig. 8.
Characterisctics analysis End Fig. 8. Flow chart for identifying floods process using three denoising.
With the rapid exploitation of the Yellow River Basin, several largescale reservoirs are planned on the upper reaches of the Yellow River, the construction and operation of which will affect the fishes greatly. It is necessary to quantify the suitable flow processes for fish survival and reproduction to guide the operation of those projects. Tangnaihe hydrological station is the first hydrometric station on the Yellow River, the river length above the control section is 1553 km, accounting for 28.4% of the whole river, as shown in Fig. 9. Although no large-scale reservoirs exist on the upper stream of the Tangnaihe station right now, there are at least three large reservoirs are under demonstration and design upstream. Thus it is significant to figure out the characteristics of the flow process at Tangnaihe station. There is no national protected fish around the Tangnaihe station. Other endangered and local protected fish mainly include: Chuanchia labiosa, Platypharodon extremus, Gymnocypris eckloni, Schizopygopsis pylzovi, Triplophysa siluroides, Gymnodiptychus pvery singleycheilus, Acanthogobio guentheri, etc. Most of the protected fish above are cold-water fish with adhesive and demersal eggs. The flowing environment of the river is an important factor for the existence of the spawning ground for these fish. Long-term observations indicate that the fish spawning requires not only flow pulses but also a continuous rising process during the pulse,
3. Case study 3.1. Study case The Yellow River is the 2nd longest river in China with the length of 5464 km and the drainage area of 752 × 103 km2. The upper reaches of the Yellow River located in the Qinghai-Tibet Plateau is a key protected area due to its importance in water conservation and ecological environment. 214
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Fig. 9. Geographical location of the fish spawning ground on the upper reaches of the Yellow River. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
fluctuation magnitude is set to be 20% of the maximal increment in the same year in this study; i.e., if Eq. (10) is satisfied, the i-th fluctuation is regarded as an effective flood process, or it is eliminated. The identification results of the double smoothing described above is shown in Fig. 11.
and there also have to be enough flood peaks. Based on the historical flow data during the fish spawning season (April to June) at Tangnaihe station from 2009 to 2016, this study identifies the flood process using the proposed methodology, based on which, the corresponding characteristic parameters are analyzed.
(max(Qi ) − min(Qi )) ≥ 20% × max{(max(Qi ) − min(Qi ))}, i
3.2. Identification of flood processes
= 1, 2, ⋯, N
In the year of 2012, there is no fluctuation need to be modified considering the relationships between adjacent processes; i.e., there is no large falling as described in Eq. (6) on the rising margin, nor large rising described in Eq. (7) on the falling margin. Also, no small fluctuation exists after the modification. The final identification result of the flood processes of the year 2012 is shown in Fig. 11.
Smoothing width in the initial smoothing is one of the few algorithm parameters used in flow process identification, which is set to be 3; i.e., the smoothing starts from the second point, and the first derivative of the second point is the average of that of the first three points 1 K2 = 3 (k + k2 + k3) . Such smoothing is carried on until meeting the (n1
(10)
1
+ kn − 1 + kn ) . The first derivatives of 1)-th point, where Kn − 1 = 3 (k n−2 the first and the last points are set to be 0, as in Eq. (2). Compared with Eq. (3), such processing does not affect the identification of peaks and troughs, and thus does not affect the identification of flood processes. Besides, processing with Eq. (2) runs faster than using Eq. (3) in smoothing. After the initial smoothing, the peaks and troughs can be extracted. Taking the year of 2012 as the example, the initial identification of peaks and troughs is shown in Fig. 10(a). The water flow process in 2012 does not exist in the situation shown in Figs. 2 and 4, but there is a situation shown in Fig. 3. After the modification of endpoints, the last flood process is identified, as shown in Fig. 10(b). After the preliminary denoising, the floods process in 2012 was represented by “left trough – peak – right trough”, as shown in Fig. 10(b). For the identification results in Fig. 10(b), there still exist small fluctuations, such as the third one, which is obvious invalid for fish spawning stimulus and needs to be omitted. The threshold of the
3.3. Identification results 3.3.1. Classification of hydrological years Pearson type III curve fitting are performed to classify the years into wet years, normal years, and dry years, respectively, and the results are shown in Table 1: 3.3.2. Identification result Using the methodology describe above, the identification results of the flood processes in the spawning season from the year 2009 to 2016 are shown in Fig. 12. 3.3.3. Characteristic parameters Based on the identification results above as well as the classification of different hydrological years, the characteristic parameters of flood 215
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1800
Flow process Peak Trough
Flow process Peak Trough
1600
1400
1400
1200
1200 Flow (m3/s)
Flow (m3/s)
1600
1800 2012
1000 800
1000 800
600
600
400
400
200
2012
200 10
20
30
40 50 Time (day)
60
70
80
90
10
(a) Before endpoint processing
20
30
40 50 Time (day)
60
70
80
90
(b) After endpoint processing
Fig. 10. Floods identification results after preliminary denoising in2012, where the blue line in the figure represents the daily flow process of the spawning season, the triangle represents the peak, and the asterisk indicates the trough. Before the endpoint processing, the rising edge in the end is excluded in the flood; while after the endpoint process, it is recognized as a part of the flood, as indicated in the red circle in (a) and (b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
1800
Flow process Peak Trough
1600
normal years (2010, 2013, 2014) are 5, 4, and 4, respectively. In comparison, longer duration with low flows lasts, thus only 3 times of flood happens in the year of 2015. In another dry year of 2016, although there are 6 times of flood processes are identified, the average flow in spawning season are apparently smaller than that in wet years and normal years. It can be concluded that 4–6 times of flood is necessary and desired for fish spawning stimulus in this case. The average duration of one complete flood process is about 11 days in both wet years and normal years. In dry years, the duration seems a little longer with the number of 12 days, while the flow is relatively smaller. The daily growth rate of the flow rising processes are 59.64 m3/(s·d), 61.97 m3/(s·d), and 60.95 m3/(s·d) in wet years, normal years, and dry years, respectively, which indicates that in wet years, the flow rises continuously with lower growth rate, while in normal years and dry years, there are several drastic fluctuations. However, the significant difference analysis is carried out on the daily growth rate of different hydrological years. The P value was 0.989, which is much larger than 0.05, indicating that there is no significant difference in different hydrological years. The average daily growth rate of the floods should be around 60.67 m3/(s·d) for fish spawning.
2012
1400
Flow (m3/s)
1200 1000 800 600 400 200 10
20
30
40 50 Time (day)
60
70
80
90
Fig. 11. Floods identification results after double denoising in 2012, where the blue line in the figure represents the daily flow process of the spawning season, the triangle represents the peak, and the asterisk indicates the trough. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
4. Conclusion The construction and operation of large-scale reservoirs changes the original flow regimes of rivers, resulting in the impact on hydrophytic habitat of aquatic organisms, especially fish. Both literatures and observations indicate that the flow regime in the spawning season are effective to stimulate fish to lay eggs for many fish species in the world. In this study, an identification methodology composed of three times of denoising is proposed to identify the effective flood processes for quantitatively analysis. The proposed method is applied on the upper reaches of the Yellow River, where no human interference occurs. The analysis on recent 8 years indicates that there indeed exists some laws for the flow regimes in the spawning seasons, even for different hydrological years. There should be 4–6 times of the flood processes in the spawning season, and each process should last for about 11 days. The desired growth rate of the flood processes is about 60 m3/(s·d). Basically, the flow rises continuously with smaller growth rate in wet years, and for dry years, there are several violent fluctuations dominating the flow regimes in the spawning season. The flow regimes are quantitively identified for the first time in this study. The proposed methodology can be generalized to any river with little human intervention, which can be taken as the reference for the river management.
Table 1 Classification of different hydrological years. Classification
Criteria
Flow threshold (m3/s)
Year
Wet year Normal year Dry year
P≤37.5% 37.5% < P≤62.5% P > 62.5%
Q > 632.17 539.86 < Q≤632.17 Q≤539.86
2009, 2011, 2012 2010, 2013, 2014 2015, 2016
processes defined in Section 2.2 are worked out, as in Table 2.
3.4. Discussion The characteristics of the identified flood process are analyzed for wet years, normal years and dry years, respectively. Basically, there are 4–6 occurrence of flood processes in both normal years and wet years, when the flow condition is deemed to be suitable for fish spawning. More floods take place in wet years (2009, 2011, 2012) with the number of 6, 6, and 7, respectively, and that in 216
Ecological Engineering 138 (2019) 209–218
1000
Flow process Peak Trough
500
1000 500
Flow (m3/s)
1500 1000
40 60 Time (day) (c) 2011
1000
40 60 Time (day) (e) 2013
1000
40 60 Time (day) (g) 2015
80
Flow process Peak Trough
1500 1000
40 60 Time (day) (d) 2012
80
Flow process Peak Trough
500 20
500
40 60 Time (day) (b) 2010
500
80
Flow process Peak Trough
20
1500
20
500
1500
500
80
Flow process Peak Trough
20
1000
Flow process Peak Trough
20
Flow process Peak Trough
20
1500
80
Flow (m3/s)
1500
40 60 Time (day) (a) 2009
Flow (m3/s)
Flow (m3/s)
20
Flow (m3/s)
Flow (m3/s)
1500
Flow (m3/s)
Flow (m3/s)
F.-F. Li, et al.
1500 1000
40 60 Time (day) (f) 2014
80
Flow process Peak Trough
500
80
20
40 60 Time (day) (h) 2016
80
Fig. 12. The final identification results of flood processes of 2009 to 2016, where the blue line in the figure represents the daily flow process of the spawning season, the triangle represents the peak, and the asterisk indicates the trough. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Acknowledgements
Table 2 Characteristic parameters of flood processes in different years. Year
N
Mean of N
−
T (d)
Mean of −
T (d) Wet years
2009 2011 2012
6 6 7
6.3
Normal years
2010 2013 2014
5 4 4
Dry years
2015 2016
Average
–
−
This research was supported by National Natural Science Foundation of China (Grant No. 91847302), National Key R&D Program of China (2017YFC0403600, 2017YFC0403602).
−
η (m3/ (s·d))
Mean of η (m3/(s·d))
50.7 64.3 63.3
59.6
11.3 10.5 10.4
10.7
4.3
8.2 12.8 11.8
10.7
65.7 70.2 49.1
62.0
3 6
4.5
12.7 11.8
12.1
58.5 62.2
61.0
–
5.1
–
11.0
–
60.7
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218
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Ecological Engineering journal homepage: www.elsevier.com/locate/ecoleng
Quantitative identification of natural flow regimes in fish spawning seasons a
a
Fang-Fang Li , Cong-Min Liu , Jun Qiu a b
b,⁎
T
College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China State Key Laboratory of Hydroscience & Engineering, Tsinghua University, Beijing 100084, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Flow regimes Quantitative identification Characteristic parameter Fish spawning Yellow River
The construction and operation of water conservancy projects changes the original flow regimes of rivers, and thus disturbs the habitat conditions of aquatic organisms. As an important aquatic living resource, fish is sensitive to the flow changes of the river, especially in the important periods such as spawning seasons. Existing studies indicates that flow regimes are effective stimulus for the fish spawning. In this study, an identification algorithm composed of three times of denoising is proposed to identify those rises with a certain magnitude and duration, which are deemed to be effective for spawning stimulus. Characteristic parameters including the number of the flood processes in the spawning season, the duration of each flood, and the growth rate of rising are statistically analyzed for the identified processes. The application on the upper reaches of the Yellow River in China, where almost no human intervention occurs, indicates that there indeed are some regularity for the flow regimes in the spawning season, even in different hydrological years. 4–6 times of floods composing a flow rising process is in need, and each of them should last for about 11 days. The desired daily growth rate is around 60 m3/(s·d) for the studied case. Such results are conductive for reservoir operation and the management of the river.
1. Introduction Natural river flow with cyclical fluctuations provides a diverse and suitable habitat for aquatic community to sustain multiple species. The flow variability plays an especially important role in different life stages of a specific species (Chang et al., 2011). The construction and operation of water conservancy projects attenuates such fluctuation, even breaks the flow, resulting in the reduction of the diversity and richness of aquatic organisms (Gao, 2006; Hardie, 2013; Coops and Hosper, 2002; Larson et al., 2016). Although replicating natural flow regimes in managed systems can be impossible, incorporating elements of the natural flow regime (like ascending baseflows) can benefit the restoration and conservation of riverine species (Goodman et al., 2018). Fish are important aquatic living resources with high trophic levels in aquatic ecosystems, and it can be a reliable indicator of the ecological balance in stream (Hur et al., 2017; Li et al., 2011; Liu and Men, 2007; Misetic et al., 2003; Suen et al., 2009; Wu et al., 2014; Zhao et al., 2017). Changes of hydrological and hydraulic factors related to river flow, i.e., flow, flow rate, water quality and hydrological conditions, affect the physiological activities of fish (Poff and Zimmerman, 2010; Shen et al., 2018). Flow regimes contain not only the flow magnitude but also other parameters such as time, the duration of the flow rise, growth rate, and ⁎
the number of flow peaks (Poff and Zimmerman, 2010), which can effectively stimulate fish for spawning (Alonso-Gonzalez et al., 2008; Chen et al., 2015; Suen and Herricks, 2009; Stanley et al., 1978; Rakowitz et al., 2008; Grabowski and Isely, 2007), and influence the spawning time and abundance of eggs and juveniles (Nagaya et al., 2008). In the study of Bailly et al. (2008), period, duration and intensity of floods were the considered flooding attributes, and they declared that intense floods favored gonad development and increased fish survival during initial development. Ozen and Noble (2002) collected Age0 largemouth bass Micropterus salmoides over seven years throughout the spawning season in Lucchetti Reservoir, Puerto Rico, and they concluded that the initiation of largemouth bass spawning was stimulated by water level increase. Zhang et al. (2015) believed that the fluctuations of water flow could effectively stimulate fish to spawn, and contributed to recovering river ecosystem diversity. Zhang et al. (2018) argued that an ecological flow regime with specific eco-hydrological signals (such as flow, frequency, duration, timing and rate of change) could not only guarantee oviposition but also meet the drifting conditions required for eggs. Agostinho et al. (2004) studied the influence of dam-controlled floods on some fish assemblage attributes, reproduction and recruitment in the Upper Parana River floodplain, and found that large floods were associated with higher species richness, more importantly, frequencies of individuals with ripe and partially spent
Corresponding author. E-mail address: [email protected] (J. Qiu).
https://doi.org/10.1016/j.ecoleng.2019.07.024 Received 11 December 2018; Received in revised form 15 March 2019; Accepted 23 July 2019 0925-8574/ © 2019 Elsevier B.V. All rights reserved.
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needs of fish community using an autecology matrix, which are directly related to the flow requirements of fish community. Sung et al. (2005) calculated ecological flow through habitat simulation, where the Habitat Suitability Criteria was developed for the two fish species (Zacco Platypus and Zacco Temmincki) and life stages (spawning and adult) and habitat conditions (depth, velocity and cover) need by fish. Various practical difficulties lie in studying fish-flow relationships from the perspective of fish demand for river flow. One of the major challenges is that the data on the demand of aquatic organisms for river flow is difficult to obtain in most cases due to the scarcity of data on aquatic ecosystems (Sun et al., 2012), and there is a lack of sufficient knowledge of the needs of fish communities. In addition, for any of the species, the specific physiological requirements for a particular element of the river flow are difficult to observe separately. Most of the previous studies have considered the relationship between the weighted available area of aquatic habitats or the quality of habitats and water flow (Boavida et al., 2018; Hsu et al., 2012; Johnson et al., 2017; Kim et al., 2016; Li et al., 2015), with little regard to the direct relationship between aquatic organisms and the flow regime. In addition, they only focused on individual fish species (e.g. Chinese sturgeon, Australian Grayling, Grass Carp). Long-term observations (more than 3 years) can hardly be found (Growns and James, 2005; King et al., 2015), which affects the universality of the results. The other perspective to study the flow demand for the fish spawning is from the flow regimes. Some publications have studied the characteristics of the hydrological statistics to determine the ecological flow (Chang et al., 2008; Chang et al., 2011; Tsai et al., 2015), which is defined as a human-modified flow regime that captures the natural flow variability for maintaining the structure and the functional integrity of the aquatic ecosystems by Suen (2011). Huang et al. (2014) proposed a flow frequency interval ratio method to calculate the ecological flow, and they divide the monthly flow series by 25% and 73% guarantee rate to 3 intervals including wet, normal and dry year, and the ecological flow of different levels is calculated in each interval. The environmental flows progressively reduce with the neglect of ecological protection, and they are relatively low for most rivers compared to environmental management objective (Smakhtin and Eriyagama, 2008). The environment required for the survival and reproduction of fish is formed through a long-term biological evolution, the relative stability of which is the prerequisite for the stability of fish diversity and abundance (Zou, 2011). Modern water resources management adopts natural flow regime as a target for river ecological restoration and protection (Chang et al., 2011; Gao, 2006; Suen, 2011; Xu et al., 2015). Sun et al. (2012) considered that the rivers were less disturbed by human activities in the 1950s, so the average daily variation of river flow during this period was taken as the target environmental flows. TGR has introduced manmade flood by ecological operation experiments to facilitate spawning of the four major Chinese carps. In this study, taking fish as the indicator of the health of aquatic ecosystems, the flow regimes in fish spawning season is identified by feature extraction on the natural flow process. Those floods with a certain fluctuation range are considered to be effective for spawning stimulus compared with small random fluctuations, which are identified by different ways of signal de-noising. The statistical characteristics of the effective floods are then drawn for different hydrological years. The methodology is applied on the upper reaches of the Yellow River, where almost no human intervention exists. The daily flow data from the year 2009 to 2016 in spawning seasons at Tangnaihe station, the very first hydrometric station of the Yellow River, are analyzed. The results indeed show some regularity features, i.e., there exist 4–6 times of floods composing a flow rising process is in need; each lasts for about 11 days, and the daily growth rate of the floods is about 60 m3/s. Such results verify the knowledge that there exist some quantitative flow regimes patterns in fish spawning seasons, which may be required by the fish spawning, and provide convictive references for river management in the future.
gonads, which indicate spawning, were higher during the period of increasing water level. Chehade et al. (2015) sought to verify experimentally which gonadal changes were present in mature individuals of Astyanax altiparanae arising from decreased water level, and found the water level drawdown applied to specimens in spawning capable phase of Astyanax altiparanae to induce reproduction was effective, especially in females. Seasonal flood peaks can be weakened or eliminated by reservoir scheduling, so that the triggering factors required for fish migration, spawning and hatching are interrupted, which is detrimental for fish reproduction (Leira and Cantonati, 2008). A study by Piana et al. (2017) found that intensity of floods determined the inter-annual variation in abundances of curimba Prochilodus lineatus, and Porto Primavera Dam negatively impacted the abundances at sites in the floodplain. Phelan et al. (2017) reported that reduced flow led to a decrease in fish abundance and diversity. Tan et al. (2010) found that flow changes of the Pearl River in China resulted in delays in fish spawning time and a decrease in fish larval abundance. Shen et al. (2018) stated that after the impoundment of the Three Gorges Reservoir (TGR), the natural river flow process changed, the habitat of the Chinese sturgeon was degraded, and its spawn time was delayed. Compared with other fish that produce drifting eggs, the four major Chinese carps (i.e. grass carp (Ctenopharyngodon idellus), black carp (Mylopharyngodon piceus), silver carp (Hypophthalmichthys molitrix), and bighead carp (Aristichys nobilis)) in the upper reaches of the Yangtze River have stricter requirements on the flow process during the breeding season. Shen (2015) conducted a statistical analysis of the relationship between the number of eggs and the index of flow rising in the Jiangjin section, and found that the scale of spawning is closely related to the process of flow rising. By correlation analysis and significant test, Shen (2015) proved that the initial flow and the daily average flow increase were the most critical indicators for the reproduction of the four major Chinese carps. The existing studies have demonstrated the importance of identifying the most suitable flow regimes for fish spawning for river management (Coops et al., 2004). There are two perspectives to study the fish demand for river flow. One starts from the fish. Koster et al. (2018) examined the influence of flow on spawning of the Australian grayling, using acoustic telemetry and drift sampling of eggs, they confirmed that egg concentrations peaked when weekly flows increased, and this information has been incorporated into the development of targeted environmental flows in the Bunyip-Tatago river system. In Girnock Burn, Aberdeenshire, Scotland, Webb et al. (2001) investigated 428 spawning events in 48 spawning sites and calculated the discharge, they found that female atlantic salmon need higher flow for spawning during the spawning season. Xu et al. (2015) investigated the four major Chinese carps spawning activities and their responses to the Three Gorges Reservoir (TGR) operation. Their statistical analysis showed that the flow effect on spawning could be expressed in three aspects (timing, amplitude, duration) by different hydrological variables: (i) the initial flood flow could affect the timing of spawning; the higher the initial flow level, the quicker the spawning response; (ii) the amplitude of flow increase could mostly affect the spawning scale (egg abundance); the higher the increase in the daily flow, the more abundant the eggs were in response; (iii) in annual variation, the spawning scale was positively correlated with continuous days of the rising hydrograph, but the relationship was very poor regarding the inter-annual data probably related to the different background in breeding populations. Taking the fish named Acrossocheilus paradoxus as the indicator species, Hsu et al. (2012) identified the most suitable value of ecological base flow of the TouQian River based on the adaptation indicators of its habitat by trade-off analysis and the incremental method. Due to the lack of natural hydrological statistics, Chang et al. (2011) used a point-biserial correlation method to relate fisheries collections with TEIS statistics, and then identified the ecological flow, which verifies the importance of fish as an indicator species on the study of ecological flow patterns. Suen and Herricks (2009) identified ecohydrological indicators by analyzing the 210
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1
Ki =
2.1. Identification of flood processes The flood process in this study refer to a complete wave with a certain amplitude and duration of fluctuation, and can be an effective stimulus for fish spawning. Simple connection of extreme points of the original sequence does not work due to the existence of a large amount of noises, as illustrated in Fig. 1. Therefore, smoothing the original time series to eliminate noises is the prerequisite for identifying flood process and extracting characteristic parameters.
y '= −y + a
i=n
(3)
(4)
2.1.1.4. Preliminary identification of the flood process. The peaks are firstly located and then the two troughs on the left and right sides of it are identified to form a complete flood process with “left trough – peak – right trough”. Such identification mechanism leads to the possibility of “trough – peak – … – peak – trough”, as shown in Fig. 4(a). The processing approach to avoid two peaks in the same flood process is that the one with a smaller value of the two peaks is eliminated after comparison of the two peaks. Hence the effective flood process with “left trough – peak – right trough” can be ensured, as shown in Fig. 4(b). It needs to be explained that there is no such thing as “trough – trough – … – trough – peak – trough – … – trough” due to the identification mechanism. The invalid troughs on the outsides will be automatically ignored, and will not affect the identification of the effective flood process.
(1)
Moving average processing takes the mean of the first derivatives at m successive points as the first derivative of the intermediate point. There are two ways to process the first (m − 1) and the last (m − 1) points, 2
as shown in Eq. (2) and Eq. (3), respectively. When (m − 1) is relatively 2 small, the first derivatives of the starting and end points is set to be 0, and the overall formula is shown in Eq. (2); while when (m − 1) is large, 2 smoothing is performed as much as possible, as shown in Eq. (3). j
⎧ 1 ∑ k , i = j + 1, j + 2, ⋯, n−j i+g m ⎨ g =−j ⎪ 0, i = 1, ⋯j, n − j + 1, ⋯n ⎩
i=1
2.1.1.3. Endpoint processing. Since the troughs and peaks are identified separately, the first or the last identified peaks may be eliminated as there is no troughs identified before or after the first or last peaks. In these cases, the starting points or the endpoint of the sequence are added to form a flood process, as illustrated in Fig. 2. Another possibility is that the flow falling process has not begun yet at the end of the defined spawning period, but the flow rising process continues and the maximal value exceeds the peak of the previous flood process. It can be regarded as an effective flow rising process stimulating fish to spawn. Thus the end point is defined as a peak in this case, and the duration of flood process is the time from the trough to the end, i.e., it can be considered that the end point is both a peak and a trough to form a complete and effective flow rising process, as shown in Fig. 3.
2.1.1. Preliminary denoising 2.1.1.1. Initial smoothing. It is difficult to applying curve fitting on the flow sequence without the knowledge of the distribution of the data on a flow process. Besides, the possibility of the flow sequences in different years obeying the same distribution is rather small. In this study, the moving average method is used to smooth the first derivative of flow sequence at first to eliminate those jagged noises with small fluctuations. The reason why the original flow sequence is not directly smoothed is that the moving average processing would modify the original information, and the identified peak and valley value would significantly differs from the original data. The first derivatives of the flow sequence at the i-th point ki is defined in Eq. (1)
Ki =
k1 + k2 , 2 kn − 1 + kn , 2
2.1.1.2. Preliminary identification of the troughs and peaks. A complete flood process includes the rising margin and the falling margin on the curve, which is described as “left trough – peak – right trough”. Thus the location of the troughs and the crests needs to be determined first. The minimums can be easily found with the help of the first derivative. However, due to the initial average smoothing, there exists an offset. Hence, after the initial smoothing which helps determine the location of the extremes within an offset of m, the trough is sequentially detected in the m adjacent points, i.e., the minimum of the m points oriented by the initial smoothing is set as the preliminary troughs. The determination of the peaks is similar, taking the opposite number of the original data and shifting the sequence upwards to avoid negative values, as in Eq. (4).
2. Methodology
2
⎨ ⎪ ⎪ ⎩
where Ki and ki indicate the first derivative at the i-th point before and after the averaging, respectively; j = (m − 1) . 2 The random variation of the first derivative {Ki} is reduced by the moving average processing, which is called initial smoothing in this study. It should be illustrated that such smoothing process can be repeated until the smoothing effect is satisfying.
Fig. 1. An example of original daily flow process in spawning season; Those fluctuations with a certain amplitudes is considered to be effective for fish spawning stimulus, as indicated by the black arrows; while those small fluctuations are regarded as noises, as indicated by the red arrows. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
y2 − y1 , i = 1; ⎧ ⎪ yi + 1 − yi − 1 , i = 2, 3, ⋯, n − 1; ki = 2 ⎨ ⎪ y − yn − 1 , i = n n ⎩
j
j + 2, ⋯, n − j ⎧ m ∑g =−j ki + g , i = j + 1, ⎪ 1 2i − 1 ⎪ 2i − 1 ∑r = 1 kr , i = 2, ⋯j, n − j + 1, ⋯n − 1
2.1.2. Double denoising Although the preliminary denoising is able to effectively remove the jagged noises, there still may exist slight fluctuations in the preliminary identification results. Only those waves with a certain amplitude of variation is considered to be effective stimulus for fish spawning. Analyzing the identification results of flood processes over the years, the difference between a peak and the left trough in a “left trough –
(2)
211
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(a) Before modification
(b) After modification
Fig. 2. Schematic diagram of the modification of the endpoints in consideration of the identification mechanism of the peaks and troughs, where the curve represents the water flow process, the triangle represents the peak, the asterisk represents the trough, and the adjacent “trough – peak – trough” represents a flood process. Before the modification, the flood at the end cannot be recognized, as in the red rectangle in (a); while after the modification, they are identified as indicated in (b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
peak – right trough” process is defined as the increment of a rising process. When the increment of a fluctuation is less than a certain percentage (which is set as 20% in this study case) of the maximum increment in the same year, it is regarded to be a small ineffective fluctuation and is eliminated in this study, as shown in Fig. 5. Such processing is called double denoising.
beginning of the rising process identified by smoothing, which is mistakenly classified as the rising margin. Such smooth processes are eliminated in this study by checking the slope of rising margin, and only those margin with the slope large enough and continuous is recognized as an effective flood process, as shown in Eq. (7). Such modification considering the slopes are illustrated in Fig. 7.
1 j
2.1.3. Modification of the troughs considering adjacent processes All the processing above directs at independent peaks and troughs without consideration of the relationship between adjacent troughs and peaks. If there are flow fluctuations on the rising margin and the decrement of the previous flood process is greater than a1 times the value of the latter increment as in Eq. (5), it is considered that the rising margin starts from the end of the previous falling margin, as shown in the dash rectangle in Fig. 6; while on the falling margin of a process, if there are flow fluctuations and the value of the latter increment is greater than a2 times the value of the previous decrement as in Eq. (6), it is considered that the falling margin ends after the previous falling, as shown in the dot rectangle in Fig. 6.
Qi > a1 × Qj
(5)
Qn > a2 × Qm
(6)
j
∑ Ki < a3 × i=1
1 n−j
n
∑
Ki
i=j+1
(7)
where n indicate the number of points of the rising margin; j-th point is the boundary between two slopes; Ki indicate the first derivative at the i-th point after moving averaging. 2.1.4. Triple denoising After the modification considering adjacent processes and slopes, another smoothing similar to that illustrated in Section 2.1.2 is taken to remove the small fluctuations, called triple denoising. 2.2. Characteristics of the flood processes In order to quantitatively describe the flow rising process and to provide quantitative reference for ecological scheduling of the reservoir, the following characteristic parameters are defined and
In addition, there may exists a rather smooth process at the
(a) Before processing
(b) After processing
Fig. 3. Schematic diagram of the modification of the end flood process in consideration of the actual water flow process, where the curve represents the water flow process, the triangle represents the peak, the asterisk represents the trough, and the adjacent “trough – peak – trough” represents a flood process. Before the modification, if the flow process ends with a rising edge, it would not be recognized as a part of effective flood as in the red rectangle in (a); while after the modification, it is identified as an effective flood, as indicated in (b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 212
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(a) Before processing
(b) After processing
Fig. 4. Schematic diagram of the modification of the “trough – peak – … – peak – trough” in consideration of the flood process, where the curve represents the water flow process, the triangle represents the peak, the asterisk represents the trough, and the adjacent “ trough – peak – trough” represents a flood process. Before the modification, the situation that multiple local maxima existing in the same flood may occur, as indicated in the rectangle in (a); while after the modification, only the highest one is reserved as the peak in a flood process, as indicated in (b).
(a) Before the double denoising
(b) After the double denoising
Fig. 5. Schematic diagram of the double denoising considering small fluctuations interference, where the curve represents the water flow process, the triangle represents the peak, the asterisk represents the trough, and the adjacent “ trough – peak – trough” represents a flood process. Before the second denoising, some small fluctuations may still exist in the identified flood process, as in the red rectangle in (a); while after the double denoising, they are omitted, as indicated in (b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
It is used to describe the mean durations of multiple flood processes in the spawning season of a certain year, as defined in Eq. (8):
analyzed: (1) The number of the flood process in the whole spawning season N ; − (2) The average duration in days of each flood process T :
−
N
T = (∑
i=1
(a) Before modification
Ti )/ N
(8)
(b) After modification
Fig. 6. Schematic diagram of the modification of the troughs position in consideration of the water flow trend of rising and fallinging, where the curve represents the water flow process, the triangle represents the peak, the asterisk represents the trough, and the adjacent “ trough – peak – trough” represents a flood process. After the modification, those fluctuations with a relatively large amplitude are excluded in a flood process. 213
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(a) Before modification
(b) After modification
Fig. 7. Schematic diagram of the modification of the troughs position in consideration of the slopes of the flow rising or falling edge, where the curve represents the water flow process, the triangle represents the peak, the asterisk represents the trough, and the adjacent “trough – peak – trough” represents a flood process. After the modification, if the slope of the rising or falling edge is relatively slow, it will be excluded in the flood process, as indicated in the red rectangle in (a) and (b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
(3) Average growth rate:
Start
It is used to describe the mean growth rate of multiple floods in a certain spawning season, which reflects both the increment and the temporal effect, as defined in Eq. (9): −
N
η = (∑
i=1
max(Qi ) − min(Qi ) )/ N Day|max(Qi ) −Day|min(Qi ) + 1
Initial smoothing
Original flow sequence
Preliminary identification of throughs and peaks
Preliminary denoising
Endpoint processing
(9)
Double denoising
−
The larger the η is, the severer a flood perceived by the fish is. Thus η is deemed to be an important parameter for fish spawning. To further analyze the stimulus of the flood to fish spawning in different hydrological years, the characteristic parameters defined above are statistically calculated in wet years, normal years, and dry years, respectively. −
Modification of troughs considering adjacent processes
Preliminary identification of flow rising processing
Triple denoising
2.3. Implementation
Flow rising processes
Both literatures and actual observations demonstrated that the continuous flood processes are the effective stimulus for fish spawning instead of small fluctuations, which is also the precondition and motivation of this study. Thus denoising is firstly operated on the first derivative of the natural flow process with moving average method to preserve the original information, known as “initial smoothing” in this study. The peaks and troughs of the smoothed process are determined to form completed “left trough – peak – right trough” flood regimes with the modification of the endpoints. To eliminate the survival small fluctuations which is invalid for spawning stimulus, a certain amplitude of the fluctuation is defined as the threshold for the secondary denosing, called “double smoothing”, by which those fluctuations with a range smaller than the threshold is omitted. Troughs are then modified again considering the relationships between adjacent processes to ensure the continuous rising and falling in one complete flood process. The identified flood processes are obtained after denoising for the third time, the “triple smoothing”, whose characteristics are quantified by the proposed parameters. The flow chart of the methodology proposed in this study is illustrated in Fig. 8.
Characterisctics analysis End Fig. 8. Flow chart for identifying floods process using three denoising.
With the rapid exploitation of the Yellow River Basin, several largescale reservoirs are planned on the upper reaches of the Yellow River, the construction and operation of which will affect the fishes greatly. It is necessary to quantify the suitable flow processes for fish survival and reproduction to guide the operation of those projects. Tangnaihe hydrological station is the first hydrometric station on the Yellow River, the river length above the control section is 1553 km, accounting for 28.4% of the whole river, as shown in Fig. 9. Although no large-scale reservoirs exist on the upper stream of the Tangnaihe station right now, there are at least three large reservoirs are under demonstration and design upstream. Thus it is significant to figure out the characteristics of the flow process at Tangnaihe station. There is no national protected fish around the Tangnaihe station. Other endangered and local protected fish mainly include: Chuanchia labiosa, Platypharodon extremus, Gymnocypris eckloni, Schizopygopsis pylzovi, Triplophysa siluroides, Gymnodiptychus pvery singleycheilus, Acanthogobio guentheri, etc. Most of the protected fish above are cold-water fish with adhesive and demersal eggs. The flowing environment of the river is an important factor for the existence of the spawning ground for these fish. Long-term observations indicate that the fish spawning requires not only flow pulses but also a continuous rising process during the pulse,
3. Case study 3.1. Study case The Yellow River is the 2nd longest river in China with the length of 5464 km and the drainage area of 752 × 103 km2. The upper reaches of the Yellow River located in the Qinghai-Tibet Plateau is a key protected area due to its importance in water conservation and ecological environment. 214
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Fig. 9. Geographical location of the fish spawning ground on the upper reaches of the Yellow River. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
fluctuation magnitude is set to be 20% of the maximal increment in the same year in this study; i.e., if Eq. (10) is satisfied, the i-th fluctuation is regarded as an effective flood process, or it is eliminated. The identification results of the double smoothing described above is shown in Fig. 11.
and there also have to be enough flood peaks. Based on the historical flow data during the fish spawning season (April to June) at Tangnaihe station from 2009 to 2016, this study identifies the flood process using the proposed methodology, based on which, the corresponding characteristic parameters are analyzed.
(max(Qi ) − min(Qi )) ≥ 20% × max{(max(Qi ) − min(Qi ))}, i
3.2. Identification of flood processes
= 1, 2, ⋯, N
In the year of 2012, there is no fluctuation need to be modified considering the relationships between adjacent processes; i.e., there is no large falling as described in Eq. (6) on the rising margin, nor large rising described in Eq. (7) on the falling margin. Also, no small fluctuation exists after the modification. The final identification result of the flood processes of the year 2012 is shown in Fig. 11.
Smoothing width in the initial smoothing is one of the few algorithm parameters used in flow process identification, which is set to be 3; i.e., the smoothing starts from the second point, and the first derivative of the second point is the average of that of the first three points 1 K2 = 3 (k + k2 + k3) . Such smoothing is carried on until meeting the (n1
(10)
1
+ kn − 1 + kn ) . The first derivatives of 1)-th point, where Kn − 1 = 3 (k n−2 the first and the last points are set to be 0, as in Eq. (2). Compared with Eq. (3), such processing does not affect the identification of peaks and troughs, and thus does not affect the identification of flood processes. Besides, processing with Eq. (2) runs faster than using Eq. (3) in smoothing. After the initial smoothing, the peaks and troughs can be extracted. Taking the year of 2012 as the example, the initial identification of peaks and troughs is shown in Fig. 10(a). The water flow process in 2012 does not exist in the situation shown in Figs. 2 and 4, but there is a situation shown in Fig. 3. After the modification of endpoints, the last flood process is identified, as shown in Fig. 10(b). After the preliminary denoising, the floods process in 2012 was represented by “left trough – peak – right trough”, as shown in Fig. 10(b). For the identification results in Fig. 10(b), there still exist small fluctuations, such as the third one, which is obvious invalid for fish spawning stimulus and needs to be omitted. The threshold of the
3.3. Identification results 3.3.1. Classification of hydrological years Pearson type III curve fitting are performed to classify the years into wet years, normal years, and dry years, respectively, and the results are shown in Table 1: 3.3.2. Identification result Using the methodology describe above, the identification results of the flood processes in the spawning season from the year 2009 to 2016 are shown in Fig. 12. 3.3.3. Characteristic parameters Based on the identification results above as well as the classification of different hydrological years, the characteristic parameters of flood 215
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1800
Flow process Peak Trough
Flow process Peak Trough
1600
1400
1400
1200
1200 Flow (m3/s)
Flow (m3/s)
1600
1800 2012
1000 800
1000 800
600
600
400
400
200
2012
200 10
20
30
40 50 Time (day)
60
70
80
90
10
(a) Before endpoint processing
20
30
40 50 Time (day)
60
70
80
90
(b) After endpoint processing
Fig. 10. Floods identification results after preliminary denoising in2012, where the blue line in the figure represents the daily flow process of the spawning season, the triangle represents the peak, and the asterisk indicates the trough. Before the endpoint processing, the rising edge in the end is excluded in the flood; while after the endpoint process, it is recognized as a part of the flood, as indicated in the red circle in (a) and (b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
1800
Flow process Peak Trough
1600
normal years (2010, 2013, 2014) are 5, 4, and 4, respectively. In comparison, longer duration with low flows lasts, thus only 3 times of flood happens in the year of 2015. In another dry year of 2016, although there are 6 times of flood processes are identified, the average flow in spawning season are apparently smaller than that in wet years and normal years. It can be concluded that 4–6 times of flood is necessary and desired for fish spawning stimulus in this case. The average duration of one complete flood process is about 11 days in both wet years and normal years. In dry years, the duration seems a little longer with the number of 12 days, while the flow is relatively smaller. The daily growth rate of the flow rising processes are 59.64 m3/(s·d), 61.97 m3/(s·d), and 60.95 m3/(s·d) in wet years, normal years, and dry years, respectively, which indicates that in wet years, the flow rises continuously with lower growth rate, while in normal years and dry years, there are several drastic fluctuations. However, the significant difference analysis is carried out on the daily growth rate of different hydrological years. The P value was 0.989, which is much larger than 0.05, indicating that there is no significant difference in different hydrological years. The average daily growth rate of the floods should be around 60.67 m3/(s·d) for fish spawning.
2012
1400
Flow (m3/s)
1200 1000 800 600 400 200 10
20
30
40 50 Time (day)
60
70
80
90
Fig. 11. Floods identification results after double denoising in 2012, where the blue line in the figure represents the daily flow process of the spawning season, the triangle represents the peak, and the asterisk indicates the trough. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
4. Conclusion The construction and operation of large-scale reservoirs changes the original flow regimes of rivers, resulting in the impact on hydrophytic habitat of aquatic organisms, especially fish. Both literatures and observations indicate that the flow regime in the spawning season are effective to stimulate fish to lay eggs for many fish species in the world. In this study, an identification methodology composed of three times of denoising is proposed to identify the effective flood processes for quantitatively analysis. The proposed method is applied on the upper reaches of the Yellow River, where no human interference occurs. The analysis on recent 8 years indicates that there indeed exists some laws for the flow regimes in the spawning seasons, even for different hydrological years. There should be 4–6 times of the flood processes in the spawning season, and each process should last for about 11 days. The desired growth rate of the flood processes is about 60 m3/(s·d). Basically, the flow rises continuously with smaller growth rate in wet years, and for dry years, there are several violent fluctuations dominating the flow regimes in the spawning season. The flow regimes are quantitively identified for the first time in this study. The proposed methodology can be generalized to any river with little human intervention, which can be taken as the reference for the river management.
Table 1 Classification of different hydrological years. Classification
Criteria
Flow threshold (m3/s)
Year
Wet year Normal year Dry year
P≤37.5% 37.5% < P≤62.5% P > 62.5%
Q > 632.17 539.86 < Q≤632.17 Q≤539.86
2009, 2011, 2012 2010, 2013, 2014 2015, 2016
processes defined in Section 2.2 are worked out, as in Table 2.
3.4. Discussion The characteristics of the identified flood process are analyzed for wet years, normal years and dry years, respectively. Basically, there are 4–6 occurrence of flood processes in both normal years and wet years, when the flow condition is deemed to be suitable for fish spawning. More floods take place in wet years (2009, 2011, 2012) with the number of 6, 6, and 7, respectively, and that in 216
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1000
Flow process Peak Trough
500
1000 500
Flow (m3/s)
1500 1000
40 60 Time (day) (c) 2011
1000
40 60 Time (day) (e) 2013
1000
40 60 Time (day) (g) 2015
80
Flow process Peak Trough
1500 1000
40 60 Time (day) (d) 2012
80
Flow process Peak Trough
500 20
500
40 60 Time (day) (b) 2010
500
80
Flow process Peak Trough
20
1500
20
500
1500
500
80
Flow process Peak Trough
20
1000
Flow process Peak Trough
20
Flow process Peak Trough
20
1500
80
Flow (m3/s)
1500
40 60 Time (day) (a) 2009
Flow (m3/s)
Flow (m3/s)
20
Flow (m3/s)
Flow (m3/s)
1500
Flow (m3/s)
Flow (m3/s)
F.-F. Li, et al.
1500 1000
40 60 Time (day) (f) 2014
80
Flow process Peak Trough
500
80
20
40 60 Time (day) (h) 2016
80
Fig. 12. The final identification results of flood processes of 2009 to 2016, where the blue line in the figure represents the daily flow process of the spawning season, the triangle represents the peak, and the asterisk indicates the trough. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Acknowledgements
Table 2 Characteristic parameters of flood processes in different years. Year
N
Mean of N
−
T (d)
Mean of −
T (d) Wet years
2009 2011 2012
6 6 7
6.3
Normal years
2010 2013 2014
5 4 4
Dry years
2015 2016
Average
–
−
This research was supported by National Natural Science Foundation of China (Grant No. 91847302), National Key R&D Program of China (2017YFC0403600, 2017YFC0403602).
−
η (m3/ (s·d))
Mean of η (m3/(s·d))
50.7 64.3 63.3
59.6
11.3 10.5 10.4
10.7
4.3
8.2 12.8 11.8
10.7
65.7 70.2 49.1
62.0
3 6
4.5
12.7 11.8
12.1
58.5 62.2
61.0
–
5.1
–
11.0
–
60.7
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