Ecohydrological evaluation for Fish spawning based on fluctuation identification algorithm (FIA)

Ecological Modelling 402 (2019) 35–44

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Ecohydrological evaluation for Fish spawning based on fluctuation identification algorithm (FIA) Jun Qiua,b, Jia-Hua Weia,b, Hao Jiangc, Fang-Fang Lid,

T

⁎

a

State Key Laboratory of Hydroscience & Engineering, Tsinghua University, Beijing, 100084, China State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining, 810016, China c China Renewable Energy Engineering Institute, Beijing, 100120, China d College of Water Resources & Civil Engineering, China Agricultural University, Beijing, 100083, China b

ARTICLE INFO

ABSTRACT

Keywords: Fish spawning stimulus Flow rising process Fluctuation identification algorithm (FIA) Fish-oriented indicators Upper reaches of the yellow River

Various studies have proven that the flow rising processes play a significant role in the fish spawning stimulus. Instead of seizing the flow rise as an integrated process consisting of a flow rising edge and falling edge, the current publication focus on the daily flow. It is necessary to identify the effective flow rising process to learn the flow feature and guide the water resources management. In this study, a Fluctuation Identification Algorithm (FIA) is proposed based on the identification of flow rising and falling edges with only three parameters in total. On the strength of those identified flow rising processes, a group of fish-oriented indicators are defined to describe the features of the rising, including the number of the rising processes in the whole spawning season, the average duration of each rising process, the daily average flow in the flow rising processes, the average flow rising ratio, the average flow increment, and the average growth rate of the flow rising processes in the spawning season. The application on the upper reaches of the Yellow river over recent 10 years verifies the validity of the proposed Fluctuation Identification Algorithm. The statistics of those indicators indicates the regularities of the flow rising processes in the fish spawning seasons in different hydrological years, which helps the ecological protection in the water resources development.

1. Introduction Stream flows that are sufficient for fisheries are usually adequate for macro-invertebrate and other aquatic life (Liu et al., 2011). As in the top position of the food chain, fish can be appropriate ecosystem indicators, providing an integrative view of the environment (Wu et al., 2014; Zhao et al., 2015a). Existing studies have demonstrated that over the whole life span of fish, some specific seasons are crucial for fish reproduction, such as spawning season, when fish community are sensitive to flow velocity and water level to spawn and for the successful survival of their eggs/ larvae (Liu et al., 2011). Chen et al. (2015) reported that the spawning activity of indigenous fish Schizothorax chongi in the Yalong River in China is sensitive to the flow variations during the spawning period. Koster et al. (2018) examined the influence of hydrological variables, such as flow magnitude, temporal variables and spatial variables on migration and spawn using data collected from 2008 to 2015 in the Bunyip –Tarago river system in Victoria, and found that rising discharge act as a cue to downstream migration and spawn. Leira and

⁎

Cantonati (2008) concluded that Water-Level Fluctuations (WLF) in lakes and rivers, especially their extent, frequency and duration, are dominant forces controlling the functioning of these ecosystems by reviewing the relevant literatures from 1991 to 2008. Ondrej and Ludek (2004) found that slow and steady flow helps the migration without spawn, while the great change of flow provokes spawning for brown trout. In China, along with the developing construction of water conservancy facilities, various evidence have also been reported that the change of hydrological conditions greatly impact the spawning of fishes. Tao et al. (2017) reported that fish aggregated into the spawning grounds for spawning during the flood and departed after the flood in the Yangtze River in China. Du et al. (2011) stated that the sites where the embryo of the Chinese sturgeon occur can be predicted with the three physical variables: embeddedness of the sediment, water velocity and river bottom elevation. Xu et al. (2015) investigated the fish spawning activities of the four major Chinese carps in the middle mainstream of the Yangtze River from May to July, and concluded that the flood amplitude and river transparency were significantly positive correlated with egg abundance, and the flooding occasions was

Corresponding author. E-mail address: [email protected] (F.-F. Li).

https://doi.org/10.1016/j.ecolmodel.2019.04.011 Received 23 January 2019; Received in revised form 9 April 2019; Accepted 11 April 2019 Available online 16 April 2019 0304-3800/ © 2019 Elsevier B.V. All rights reserved.

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significantly and negatively correlated with the time of spawning. According to the book “Freshwater Pisciculture in China”, mainly edited by Academician Liu Jiakang, there is a certain velocity required to enable fish to spawn (Liu and He, 1992). The calculations based on the observed data, as well as the result of the research on fish ecology, the appropriate velocity for most fish to spawn is about 0.3 - 0.4 m/s (Liu, 1999). There are two perspectives to understand the flow-ecology relationships. One is to estimate the timing and magnitude of fish movements relative to environmental factors, which is from the perspective of the fish, such as Adapted Ecological Hydraulic Radius Approach (AEHRA), Habitat Suitability Index (HSI) and Index of Biological Integrity (IBI). AEHRA selects key fish species subjectively, thus uncertainties will be unavoidably be introduces in e-flows assessment (Zhao et al., 2017). Although Zhao et al. (2015b) developed a multi-species-based HSI (MHSI) model to estimate responses of multispecies to a habitat environmental factor, the omission of the relative frequency of available habitat in MHSI brought uncertainties to findings. Although IBI has been widely applied across the world (Wu et al., 2014), its direct application is problematic in regions having a rich and diverse ichthyofauna and where the knowledge of fish species ecology is incomplete (Tejerina-Garro et al., 2006). These studies based on fish either used fish captures at dams, ladders, weirs, and traps to quantify migration timing, or make and recapture techniques (Vehanen et al., 2000; Heggenes and Traaen, 1988). The difficulty lies in: (1) to capture free-range fish; (2) to quantify the movement of the fishes; (3) to collect adequate data over long-term monitoring. Besides, such studies introduced uncertainties derived from the selection of the representative fish species, and the studies often focused on single events with few examples, which limits the temporal applicability and robustness of findings (Koster et al., 2018; Growns and James, 2005). The other way is from the perspective of flow. The oldest and the most studied element of the flows is “biological minimum”, taking the minimum discharge for maintaining river scale as large as possible (Chen et al., 2012). While, it was realized that ecological status is influenced by a set of other hydrological features, corresponding to “appropriate ecological flow” (Barbalic and Kuspilic, 2015). More than 200 methods for assessing ecological flow were reported (Liu et al., 2011), such as methodologies of Tennant, River 2D, and Indicators of Hydrological Alterations (IHA). Although a steady-state solution can be derived from River 2D, the unattainable discrepancy of velocities and depth between the measured value and the River 2D yielded values occurs (MarkGard, 2009; Boavida et al., 2013). IHA (Richter et al., 1997) consisting of 33 parameters is considered to be good descriptors of hydrological regime characteristics which influence ecological status of rivers. As in IHA, some studies use high/low pulse of flow to describe the characteristics of flow alteration, i.e., finding peaks before a “global” rise and after a “global” decrease. Such statistics regard the flow pulses as independent occurrence, and every flow pulse is counted. The significant merit of IHA lies in its independency of a variety of other data except for daily streamflow. Nevertheless, these studies are not fish-oriented without specific identification of the key factors influencing the spawning or migrating. In this study, the flow process in fish spawning season is studied as a continuous and complete flow regime with some white noises. Several criteria are proposed to distinguish the noise and the fluctuation effective for fish spawning stimulus. Based on the proposed Fluctuation Identification Algorithm (FIA), a group of fish-oriented indicators is presented to describe the characteristics of flows in the specific crucial seasons for fish reproduction. The natural flow process in the spawning season is firstly analyzed by FIA, and the significant flow increasing process is identified, which has been proven to be a crucial stimulus signal for fish to spawn. The feature of the flow increasing process is then extracted by a group of indicators proposed in this study. The upper stream of the Yellow river in China, where almost no disturb of human activities occurs, is selected to verify the proposed method. The

selected years are classified into dry, normal and wet years, and the indicators for each kind of years are presented and analyzed. 2. Methodology 2.1. Fluctuation identification algorithm (FIA) 2.1.1. Identification of flow rising edge The rising edge and the falling edge of natural flow increasing process are identified and extracted respectively. The flow rising edge is classified into three modes, including single sharp rising, continuous rising, and follow-up rising. The single sharp rising refers to a flow rising with an amplitude larger than a certain threshold, as defined below, and there is no following flow rising subsequently. The continuous rising is composed of multiple single sharp risings with small drops in between, and the amplitude of these drops needs to be smaller than a certain threshold, as defined below. The follow-up rising refers to the situation that a sharp rising is followed by multiple small and successive risings. The original flow sequence is firstly preprocessed by the “findpeaks” function in Software Matlab to find all the local valleys and local peaks Qpi . Then the three modes illustrated above are identified successively, and the details are illustrated as follows: (1) Step 1: identification of single sharp flow rising process The natural flow process is a non-stationary time series and the fluctuation range is quite different. Small fluctuations do not produce reproductive stimuli to fish and thus are not taken into account in the identification of rising flow. Only those rising process with a certain degree of fluctuation is considered. The flow increment Qri described in Fig. 1 and Eq. (1) is used as an indicator to identify the rising edge of the flow, as shown in Eq. (2):

Qri = Qpi

Qvi

(1) (2)

Qri > Qr _ cri

where Q is the streamflow; the index r refers to the variables related to the flow rising edge; i is the index of the number of the flow rising processes in a certain year; the subscript p and v refer to peak and valley values in the flow process, respectively; Qr _ cri is the increment threshold, and if the increment Qri is larger than Qr _ cri , the corresponding rising process is recognized as an effective stimulus for fish spawning. Increment threshold Qr _ cri relates to the maximum value of the flow process, and is also relevant to the hydrological characteristics of the year. Thus Qr _ cri should be noted QrH_ cri , but for the sake of simplicity, the dependency to H is implicit, and Qr _ cri is defined as the mean of the maximum flow of the similar hydrological years, i.e., wet years, normal years, or dry years, as shown in Eq.(3):

Qr _ cri =

1

mean {max(QHm )} m

Fig. 1. Schematic diagram of single sharp flow rising process. 36

(3)

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this study 2 = 0.10 . If the change of flow in the adjacent rising processes determined in the first step is relatively gentle, the condition in Eq. (4) is satisfied, and it is recognized as the same continuous flow rising process, as shown in Fig. 3(b); while if the flow fluctuation in the adjacent flow rising processes is drastic, Eq. (4) cannot be satisfied, and it is identified as two independent rising processes, as illustrated in Fig. 3(c). Fig. 4 shows the natural daily flow series during April to June at a certain hydrologic station, where there exists a natural spawning ground downstream, and the laying season lasts from April to June. If only step 1 is carried out to identify rising flow process without recognition of continuous rising flow process in step 2, the increasing process indicated in the red circle will be judged as two independent rising processes, as shown in Fig. 4(a). However, such result is obviously inconsistent with empirical understanding of the flow incremental stimulus for fish spawning. Adding the recognition of step 2, the two adjacent processes in red circles in Fig. 4(a) is considered to be the same continuous rising process, as shown in Fig. 4(b). Fig. 2. Rising edge identification results using single sharp rising criterion.

(3) Step 3: identification of follow-up flow rising process

where 1 is the sensitive factor of the identification of flow rising process; H is the index of different hydrological years, and H = 1, 2, 3 indicate wet years, normal years, and dry years, respectively; m is the index of the year. The smaller 1 is, the more likely the flow increment is identified as a single sharp flow rising process. By our tests, the recommended value of 1 is between 0.15 to 0.35, and in this study 1 = 0.20 . Fig.2 is the daily flow sequence of a certain hydrometric station during the fish spawning season, where the red circle indicates the flow incremental process identified by single sharp rising identification.

After the foregoing two steps, the derived flow rising edge might link to small fluctuations, and in such case the rising process is considered to end. Only when the subsequent increase rate becomes more and more severe, the rising process is deemed to continue. Eqs. (5) and (6) describe the criteria above for judging the end position of the rising edge by the characteristics of subsequent rising process.

Qpi, j > Qpi, j = 1,2, …n

where indicates the j-th follow-up peaks in the i-th flow rising process, as illustrated in Fig. 5. When the fluctuation begin to decline, the flow rising process can be judged to end, as Eq. (5) cannot be satisfied. However, the case that a local peak links to a steady fluctuation instead of a sudden decline is not excluded by Eq. (5), which should also be the end of a rising process. In case the follow-up rising process is too gentle to be included in a rising process for spawning stimulus, the criteria of classifying the follow-up peaks into the rising process needs to be enhanced, as shown in Eq. (6):

(2) Step 2: identification of continuous flow rising process The natural streamflow accomplishes the rising process on the basis of fluctuations. The same rising process may consist of multiple continuous fluctuations that are connected end to end. Only using fluctuation amplitude Qr _ cri as the criterion may lead to misjudgment which recognizes the same continuous rising process as multiple independent rising processes, as shown in Fig.2. Therefore, if the “rising edge” of the adjacent flow rising process identified in the previous step is continuous, it is necessary to determine whether the two rising edges are independent. Another sensitive factor of the identification of continuous flow rising process 2 (0 < 2 < 1) is set in this study to recognize continuous rising process with the help of the flow increment Qri defined in the previous step, as shown in Fig. 3(a) and Eq. (4).

Qif

Qpi, j

Qfi, j

1)

>

3 (j

1) e j j

1

Qr _ cri , j = 1,2, …n

By convention, Qpi,0 = Qpi . In Eq. (6), (j

where is the flow decrement; 2 is the sensitive = factor of the identification of continuous rising process. The larger 2 is, the more likely the flow is identified as a continuous flow rising process. By our tests, the recommended value of 2 is between 0.1 to 0.15, and in

Qif

Qpi,(j

1) e j 1 j

(6)

is a criterion enhancement

factor, whose value increases exponentially with the progressive increase of j. This factor is used to ensure that the flow rising edge derived from step 1 and step 2 links to a follow-up rising process whose increment becomes more and more severe, when the same rising process is judged to continue. The parameter 3 is the sensitive factor of the identification of follow-up flow rising process, which is set to be 0.1 in this study by our tests. The smaller 3 is, the more likely a steady followup process is included in the rising process. In addition, there should not be any superposition of the rising edges to avoid repeated statistics of the flow rising processes, that is,

(4)

2 Qr _ cri

(5)

Qpi, j

Qfi,(j 1)

Fig. 3. Schematic diagram of (a) continuous flow rising process; (b) the case identified as one continuous rising process; (c) the case identified as two independent rising processes. 37

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Fig. 4. Comparison of identification results of continuous rising process (a) without addition of step 2; (b) with addition of step 2.

more reasonable going with our subjective judgment to be a spawning stimulus for fish. 2.1.2. Identification of flow falling edge (1) Step 4: identification of the first flow falling edge Due to the continuity of the rising and falling process of the streamflow, each identified rising edge is followed by a falling edge, which is extracted directly, and named “the first falling edge”, as shown in Fig. 7 and the red circle in Fig. 8.

Fig. 5. Schematic diagram of the follow-up flow peaks in a rising process.

the subsequent peak is always before the following rising process, as indicated in Eq.(7).

Day|Qpi, j < Day|Qpi+ 1

(2) Step 5: identification of the follow- up flow falling edge

(7)

There may exists subsequent falling process following "the first flow falling edge", as shown in Fig. 9. If the follow-up falling process is connected to a smooth flow process with small fluctuation, the process is considered to end after the first falling edge; while if continuous falling occurs, and the falling the process is considered to continue. Eq. (8) and (9) describe the criteria above for judging the end position of the falling edge based on the characteristics of the subsequent falling process, which are: (1) the peak value of the local fluctuations in the subsequent falling process cannot exceed the peak of the rising edge, as in Eqs. (8) and (2) the follow-up falling process should not be too steady, otherwise, the whole rising process is considered to end. To

where Day| indicates the julian date when occurs. Fig. 6 compares the identification results of the flow rising process with and without step 3. Fig. 6(a) shows the flow rising process identified by step 1 and step 2 without the follow-up rising process recognition, where the first decline is considered to be the end of the rising process in the red circle. Such result is clearly inconsistent with empirical judgments. Fig. 6(b) takes the subsequent flooding process into account, and assumes that a large flow peak following a small flow fluctuation is the end of the rising edge of this rising process, as indicated in the red circle. The rising process identified in Fig. 6(b) is

Fig. 6. Comparison of identification results of rising process (a) without addition of step 3; (b) with addition of step 3. 38

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Fig. 7. Schematic diagram of the first falling edge of (a) single sharp flow rising process, and (b) continuous flow rising process.

increasing amplitude the flow rising process effective for fish spawning stimulus is recognized to continue by Eq. (9), as shown in Fig. 10(b), which is apparently more reasonable. 2.2. Characteristic indicators of the flow rising process For the identified natural flow rising process, characteristic indicators need to be defined to describe the features of the rising process. The physical characteristics represented by those indicators in common in different hydrological years can be regarded as the key factors for fish spawning stimulus. If there is l flow rising processes in a spawning season in total, in which the j-th rising process covers nj daily flow data, and the whole spawning season lasts for s days. The parameter set in this study to characterize the flow rising process includes: (1) N, the total number of rising processes in the spawning season. (2) T, the average duration in days of each flow rising process in the spawning season. (3) Q¯ , daily average flow in the flow rising processes in the spawning season as defined below:

Fig. 8. The first falling edge of a continuous flow rising process.

enhance the requirement of identifying the follow-up falling process as j 1 a part of the rising process, e is taken as the amplification factor, as

Q¯ =

j

shown in Eq. (9). If j = 1, Eq. (9) is used to describe the first follow-up peak, which is the same as Eq. (8); while if j > 1, Eq. (9) is stricter than Eq. (8) to determine whether the follow-up peaks can be identified as the falling edge of the flow rising process.

Qpi, j < Qpi, j = 1,2…n ej

1

j

Qpi, j < Qpi, j = 1,2…n

l j=1

nj i=1 l j=1

Qij

nj

(11)

where Qij is the flow of the i-th day in the j-th rising process. (4) ¯ , average flow rising ratio in a spawning season:

(8)

It is defined as the ratio between the average flow in the flow rising processes and the average flow in the whole spawning season, describing the relative intensity of the flow rising, as in Eq. (12):

(9)

Fig. 10 compares the recognition results with and without considerations of the subsequent falling process. The falling process in Fig. 10(a) satisfies the conditions defined in Eq. (8), which is that the peak in falling process is smaller than that in the rising edge, and the subsequent valley is smaller than the first flow falling edge. Hence, it is identified as the same flow falling process. However, the rising process is clearly not ended. Since the falling process continues with ever-

¯ = Q¯ /( (5)

s i=1

s

Qi

)

(12)

¯ r , average flow increment in a spawning season, which reflects Q the absolute intensity of the flow increment, as shown in Eq. (13):

¯r =( Q

l j=1

[max(Qij )

min(Qij )])/l, i = 1,2…n

(13)

Fig. 9. Schematic diagram of (a) follow- up flow falling edge (b) the case identified as the falling edge of one rising process; (c) the case cannot be identified as one rising process but the end of a rising process. 39

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Fig. 10. Comparison of identification results of flow falling edge (a) without addition of step 5; (b) with addition of step 5.

Fig. 11. Study area.

Yellow River Basin, several large-scale hydropower stations have been planned in the upper reaches of the Yellow River. The development and construction of these hydropower stations will affect the fishes in the corresponding river reaches greatly. It is necessary to analyze the natural flow processes which are suitable for fish survival and reproduction to guide the operation of those projects. The Tangnaihe hydrological station is an important control station in the upper reaches of the Yellow River. The river length above the control section is 1553 km, accounting for 28.4% of the whole river. 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. The gymnodiptychus pvery singleycheilus migrate against the stream and spawn after the ice melt in April, while all the other kinds of fish spawn from May to June. Most of the protected fish above are cold-water fish, spawning 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, and there also have to be enough flood peaks. In addition, the spawning grounds of the above fish are mostly slow-flow water area with pebbles and gravel sediments, i.e., spawning grounds also have certain requirements on flow velocity and water depth, which can be reflected through the flow. Besides the dynamic characteristics of the flow above, appropriate water temperature is also an essential condition for the survival and hatching of the fish eggs. Taking the Yehuxia spawning grounds around the Tangaihai station

Table 1 Flow frequency curve parameters of the Tangnaihai station. Sample mean Ex

Variation coefficient Cv

Skewness coefficient Cs

Cs/Cv

619.18

0.26

1.43

5.5

(6) V¯ , Average growth rate, which reflects both the increment and the temporal effect, as in Eq. (14):

V¯ = (

l j =1

max(Qij ) Day|max(Qij )

min(Qij ) Day|min(Qij ) + 1

)/ l, i = 1,2…n

(14)

The larger the V¯ is, the more severe the fish perceives the flow rising. Thus V¯ is deemed to be an important parameter for fish spawning. 3. Study case (China Renewable Energy Engineering Institute, 2017) The Yellow River ranks as the sixth-largest river in the world with a total length of 5464 km. The source area of the Yellow River is located at the eastern edge of the Qinghai-Tibet Plateau, and is bordered by the cross-section of the Tangnaihai hydrological station. The drainage area of the source area is 121,972 km2, accounting for 16.2% of the area of the Yellow River Basin. The average annual runoff of the source area is 2 × 1010 m3, accounting for 34.5% of the total runoff in the Yellow River Basin. The upper reaches of the Yellow River provide abundant water resources. In recent years, with the rapid development of the 40

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Fig. 12. Flow rising process identification results of the natural daily flow at the Yehuxia fish spawning ground during the spawning season in different years.

41

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4.2. Identification results of the flow rising processes

Table 2 Characteristic indicators of flow rising processes in wet years. Characteristic indicators

2009

2011

2012

Annual average

N T (day) ¯ Q¯ (m3/s) ¯ r (m3/s) Q V¯ (m3/s/day)

4 12.75 1.05 777.60 305.96 55.82

6 10.33 1.22 808.00 384.36 54.80

7 9.86 0.97 750.99 284.41 49.22

5.67 10.98 1.08 778.86 324.91 53.28

Based on the natural daily flow of the Yehuxia fish spawning ground during the spawning season (from April to June) in the past 10 years (2007–2016), the flow rising processes are identified for each year. The results are shown in Fig. 12, where identified flow rising processes are labeled by pentagrams. The three pentagrams in the same color represent the same rising process, indicating the start point, the peak point, and the end point, respectively. These three important points in a rising process are linked by dash lines. In the same flow sequence, different rising processes are indicated by different colors. Taking the year of 2007 as the representative of the normal years, it can be seen in Fig. 12(a) that from the 1st to the 50th day, the flow process kept steady around 300 - 400 m3/s without obvious rising process. On the 51st day, there began a small flow rising process, followed by a moderate rise, and then a strong rising process occurred. These three flow rising processes are precisely identified by the proposed algorithm. Taking the year of 2009 as the representative of the wet years, Fig. 12(c) shows that there are four flow rising processes identified by the proposed algorithm, resulting the flow rise from 350 m3/s at the beginning of the spawning season to 1400 m3/s at the end. Taking the year of 2015 as the representative of the dry years, Fig. 12(i) indicates that the obvious flow rise began on the 52nd day, followed by three identified rising processes. It should be illustrated that the starting point of a slow and steady rising process still needs to be clarified with the ecological knowledge. For instance, for the year of 2008, and 2013 in Fig. 12, the starting points of the first flow rising processes are not exclusive. Besides, the situation that the last flow rising process does not end with a falling edge should be dealt with current ecological knowledge, as shown in Fig. 12(b), (d), (f) and (i).

Table 3 Characteristic indicators of flow rising processes in normal years. Characteristic indicators

2007

2008

2010

2013

2014

Annual average

N T (day) ¯ Q¯ (m3/s) ¯ r (m3/s) Q V¯ (m3/s/day)

3 11.33 1.48 879.72 781.19 70.23

4 11.50 1.14 451.36 201.87 34.66

6 7.33 1.32 738.88 276.40 63.39

4 11.75 1.38 810.24 330.21 58.60

5 11.80 1.21 715.28 322.18 50.31

4.4 10.74 1.31 719.10 382.37 55.44

Table 4 Characteristic indicators of flow rising processes in dry years. Characteristic indicators

2015

2016

Annual average

N T (day) ¯ Q¯ (m3/s) ¯ r (m3/s) Q V¯ (m3/s/day)

3 12.33 1.48 685.15 451.06 47.15

5 9.60 1.15 540.81 259.52 67.10

4 10.97 1.31 612.98 355.29 57.13

4.3. Characteristics of the identified flow rising process

as the study subject, as shown in Fig. 11, the fish spawning season lasts from April to June. The key ecological requirements for the water environment mainly include: (1) flow requirement: the sexual maturation of the fish requires stimulation of flow rising process with daily duration; (2) water depth requirements: the hatching of the adhesive and demersal eggs requires a gentle shallow water with hourly duration, and thus the flow fluctuation within one day should not be too large; (3) water temperature requirement: the spawning as well as the survival and hatching of the eggs needs appropriate temperature, and the suitable temperature for the protected fish spawning ground is about 14–15 °C. Under the gentle and shallow water condition, the river reach can receive sufficient sunniness during the spawning season, and its water temperature can satisfy the demand. Therefore, we focus on analyzing the characteristics of the flow process.

According to the classification results of different hydrological years, the flow rising characteristic indicators of the identified rising processes in different years are extracted, as shown in Tables 2, 3 and 4 . The annual average of each indicators in different hydrological years are compared in Fig. 13. It can be seen in Tables 2–4 and Fig. 13(a) that more rising processes occur in wet years than normal years and dry years, basically there are 4–6 times of rising processes in the fish spawning season. However, even for different hydrological years, each flow rising process lasts for about 11 days as indicated in Fig. 13(b). In the wet years, the flow in the whole spawning season is generally high, and thus the daily average flow in the rising processes are higher than normal years in Fig. 13(c). Compared to the flow in the whole spawning season, the flow increment in the rising processes are not as high as those in the normal years and dry years, thus the average flow rising ratio ¯ = 1.08 is relatively small in wet years. Under the assumption that the flow condition in the normal years are the most suitable for fish spawning, the daily average flow in the flow rising process should be about 30% higher than the daily average flow in the whole spawning season as shown in Fig. 13(d). It is interesting that almost all the other indicators show monotone features with the orders of wet years, normal years, and dry years, ¯ r in Fig. 13(e). Q ¯ r is much except for the average flow increment Q higher in the normal years than that in the wet years and dry years. It has not been clear that whether the fish response to the flow or to the flow increment. If the fish preferred most the flow condition in the normal years, they might be most sensitive to the flow increment instead of the flow as indicated in Fig. 13(e). In Fig. 13(f), the average growth rate in normal years is 55 m3/s/day, falling between that in the wet years and dry years. It can be inferred that the flow rising rate should not be too low nor too high to stimulate fish spawning.

4. Results and discussion 4.1. Classification of different hydrological years The streamflow generally complies with the Pearson III probability distribution, and the statistical parameters and the design values of each frequency determined by frequency analysis are taken as the criteria for the classification of hydrological years (Cohen, 1979). The flow sequence are classified into wet years, normal years, and dry years. Based on the historical flow from 2006 to 2016 at the Tangnaihai station, the frequency curve is plotted, and the corresponding parameters are shown in Table 1. According to the frequency parameters, the ten years between the year of 2007 to 2016 are classified into wet years, normal years, and dry years. Half time of the recent 10 years lasting from 2007 to 2016, the normal years are normal years, and wet years and dry years account for 30% and 20%, respectively. 42

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Fig. 13. Comparison of average annual rising characteristic indicators in different hydrological years.

It needs to be illustrated that the proposed FIA method are suitable to analyze the flow regime of the rivers with little human influences.

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5. Conclusion A Fluctuation Identification Algorithm (FIA) is proposed in this study to identify integrated flow rising processes effective for fish spawning stimulus consisting of flow rising and falling edges. A group of indicators describing the features of the identified rising processes are also defined. The application on the upper reaches of the Yellow river over recent 10 years indicates that: about 4–6 times of flow rising processes occur in the fish spawning season, and it occurs more frequently in wet years; but even for different hydrological years, each rising process lasts for about 11 days. Under the assumption that the flow condition in the normal years are the most suitable for fish spawning, the daily average flow in the flow rising process should be about 30% higher than the daily average flow in the whole spawning season, and the growth rate should not be too low nor too high with a preferred value of 55 m3/s/day. The proposed methodology can be used to analyze the flow regimes of rivers under natural conditions, and provide the knowledge of the flow characteristics preferred by fish in the spawning season. In the future, more observation of fish spawning can be carried on to check whether the rising processes detected by FIA gives an effective stimulus for spawning, for example, whether it provides a good estimation of spawning times. Acknowledgments This research was supported by National Natural Science Foundation of China (Grant No. 51879137, 91847302), and National Key R&D Program of China (2017YFC0403600, 2017YFC0403602). 43

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