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Effects of turbulence and velocity on the movement behaviour of bighead carp (Hypophthalmichthys nobilis) in an experimental vertical slot fishway
Ecological Engineering 127 (2019) 363–374
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Effects of turbulence and velocity on the movement behaviour of bighead carp (Hypophthalmichthys nobilis) in an experimental vertical slot fishway Junjun Tana, Zhu Gaob, Huichao Daia,c, Zhongyong Yanga, Xiaotao Shia,
T
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a
Engineering Research Center of Eco-environment in Three Gorges Reservoir Region, Ministry of Education, China Three Gorges University, Yichang, Hubei 443002, China College of Transportation, Nantong University, Nantong, Jiangsu 226019, China c College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, Jiangsu 210098, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Bighead carp VSF Hydraulic distribution Fish movement trajectories TKE
The vertical slot fishway (VSF) plays a major role in protection and restoration of migratory fishes at many migratory barriers. However, the VSF design greatly depends on fish response to hydraulics and swimming behavior, which are key to the design of effective VSFs for target fish species. This study evaluated the effects of hydraulic characteristics on movement behavior of bighead carp (Hypophthalmichthys nobilis) by combining spatial hydraulic distributions with fish movement trajectories in an experimental VSF. Fish spent more transit times in ranges of velocity (V) with 0.15–0.45 m/s, turbulent kinetic energy (TKE) with 0.020–0.043 m2/s2, turbulent dissipation rate (TDR) with 0.020–0.065 m2/s3and strain rate (SR) with 2–7 s−1 under different discharges (18.0L/s and 26.0L/s), respectively. Correlation were found between fish transit times and hydraulic variables of velocity (V) (Q = 18.0 m/s: r = 0.397, P < 0.01; Q = 26.0 m/s: r = 0.407, P < 0.01), turbulent kinetic energy (TKE) (Q = 18.0 m/s: r = 0.476, P < 0.01; Q = 26.0 m/s: r = 0.469, P < 0.01). TKE and V were the key hydraulic parameters associated with the fish transit movement times in VSF. The results provided key hydraulic parameters for bighead carp movement in VSFs, which can serve as reference design for the future VSF and others fishway designs for this species.
1. Introduction The natural flow regimes of rivers have been significantly altered by the construction of engineered structures such as dams, weirs and other migratory barriers, which reduced ecological connectivity of river systems, fragmented migration routes, and in many cases caused the rapid decrease in abundances of freshwater fishes, especially on those that complete their migrations within river systems (Poulet, 2007). In order to successfully restore regulated river systems, technical fishways have been developed to enhance upstream passage of fish (Katopodis, 2005; Tan et al., 2016), which can provide a significant role in enhancing river connectivity and effective upstream migration of fish. The VSF is one of the most frequently used technical fishway designs for facilitating fish passage at larger artificial barriers, such as dams, weirs, or sluices (FAO/DVWK, 2002; Shi et al., 2015). The main advantage of the VSF is that the hydraulic characteristics in this design are quasi-independent from the discharge or water depth variation in the fishway (Chorda et al., 2010; Rajaratnam et al., 1992). Research on VSFs has been focused primarily on obtaining suitable hydraulic design and operation criteria for fish passage (Rajaratnam et al., 1992; Wu
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et al., 1999; Puertas et al., 2012; Bombač et al., 2015; An et al., 2016). However, the research and design work is complex, in that it must consider practical hydraulic design criteria based on the ability and willingness of fish to seek and accept the hydrodynamic conditions presented therein (Katopodis and Williams, 2012). Also, advances in biologically-based oriented fishway research (e.g. preferences of fish movement or pathway within a fishway) were including anadromous fish species (especially salmonid species) and potamodromous cyprinids (Romão et al., 2018a,b), etc. Little research has been performed to date on passage of potamodromous Asian fishes (e.g. Asian carps) and is therefore required for effective VSF design for these species. The requirement is highlighted in Yangtze River basin in China where potamodromous Asian fish species are frequently the most abundant fish species. Black carp (Mylopharyngodon piceus), grass carp (Ctenopharyngodon idella), silver carp (H. molitrix) and Bighead carp (Hypophthalmichthys nobilis), are the most commercially important freshwater Asian carp species in China, particularly in the Yangtze River basin. Historically, the catch of these four species in the Yangtze River accounts for 63% of the total natural fish production in all of China (Wu et al., 1992). They
Corresponding author. E-mail address: [email protected] (X. Shi).
https://doi.org/10.1016/j.ecoleng.2018.12.002 Received 10 May 2018; Received in revised form 24 November 2018; Accepted 2 December 2018 0925-8574/ © 2018 Published by Elsevier B.V.
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pool to allow fish to adjust the flume conditions prior to release. Thermostatic laboratory controls and a heater/chiller were used to regulate the experimental water temperature. Free surface elevations in the VSF were measured using a point gauge to ± 0.1 mm.
are typical potamodromous fish, both adults and juveniles have extensive spawning and feeding migrations (Hu et al., 2015). However, the abundance of these carps dropped to just 4.2% of the historic average abundance in 2005 (Duan et al., 2009) by the widespread sluice gate installation for water conservancy projects. It is urgent to provide effective fish passage for these potamodromous fish. In addition, Asian carp have been introduced to many countries for biological control in aquaculture, and have had a negative impact on many native species and ecosystems (Nico et al., 2005; Wittmann et al., 2014; Hoover et al., 2017). Bighead carp was introduced in the 1970s and have been reproducing since the 1980s due to their fast growth rate and efficient filter feeding (Kolar et al., 2005). They have been found to have the potential to outcompete native species such as gizzard shad (Dorosoma cepedianum, Lesueur 1818), bigmouth buffalo (Ictiobus cyprinellus, Valenciennes 1844) (Sampson et al., 2009). In response to this situation, management decided to control and research to prevent further spread of bighead carp, particularly to the Mississippi River (Hoover et al., 2017). Using fish movement behavior to design effective hydraulic structures is a potential method for successful fish passage and reducing the spread of invasive Asian carp (Newbold et al., 2016). Although their swimming ecology has been assessed in recent studies (Newbold et al., 2016), knowledge of these species’ movement behavior response to hydraulic characteristics is sparse. Hydraulic variables including flow turbulence (turbulent kinetic energy [TKE], turbulent dissipation rate [TDR], and strain rate [SR]) (Liao, 2007; Silva et al., 2011; Gao et al, 2016) and velocity (V) are known to be closely related to fish swimming abilities, and affect passage performance (Pavlov et al., 2000; Silva et al., 2011). Therefore, it is necessary to understand how the above hydraulic parameters affect fish movement within fishways and to develop fundamental guidelines for future fishway designs. However, it has proven to be extremely difficult to get accurate measurement of both hydraulic and biological performance parameters in the field (Silva et al., 2011). Controlled laboratory experiments can be conducted on homologous conditions, allow direct observation of fish movement behavior, and also allow the control of potential confounding variables (Santos et al., 2014). The primary objective of this study was to understand the movement behaviour of one of the four Asian carp species – bighead carp – in response to four hydrodynamic indicators (V, TKE, TDR, SR) under controlled conditions in an experimental VSF. Specific questions to be addressed include: 1) which hydraulic ranges were fish preferences relate to fish movement trajectories and transit time within fishway? and 2) Which were the key hydraulic parameters that most significantly affected passage performance in VSFs for this species?
2.2. Hydraulics Experimental conditions were given in Table 1. Detailed instantaneous velocity measurements were monitored using a SonTek 16 MHz Micro acoustic Doppler Velocimeter (ADV) oriented vertically down, to measure the three-dimensional velocity components (x, y, z). The probe was positioned in the flow with an adjustable traverse and caused negligible disturbance to the observed flow. Sampling frequency of the ADV was 50 Hz for a sampling period of 60 s for each point. Velocity measurements were taken in the third pool downstream of the fishway exit because flow was fully established in this pool and velocity conditions at the entrance and the exit of this pool were similar. Measurements were taken at three horizontal planes parallel to the flume bottom, namely z = 0.3 h, 0.5 h and 0.7 h (h was the pool mean depth). In detailed, 0.3 h (9 cm), 0.5 h (15 cm), 0.7 h (21 cm) for 18.0L/s and 0.3 h (12 cm), 0.5 h (20 cm), 0.7 h (28 cm) for 26.0L/s were considered for measurements. Flow velocities were measured in a total of 102 points in each horizontal plane located 0.025–0.05 m apart (Fig. 1c). In addition, velocity at the relative depth (0.4 h, 0.6 h, 0.8 h), which parallel to the flume bottom, was measured at two given points A1, A2 under two flow conditions (Fig. 1c). So in measurement points A1 and A2 totally have measured six relative depths (0.3 h, 0.4 h, 0.5 h, 0.6 h, 0.7 h and 0.8 h). Totally, 312 instantaneous measurement points were recorded. Turbulence is one of the major variables that influences the performance of fish movement behavior (Lupandin, 2005). And it is the time and spatially dependent heterogeneous motion of rapid flow velocity fluctuations coming from the multiple chaotic water flow (Liu et al., 2006). TKE, TDR and SR are metrics that can be used to define the effect of turbulence on fish (Silva et al., 2011; Gao et al., 2016) that can be calculated based on the instantaneous velocity. TKE was related velocity fluctuation at a given point, and defined as:
TKE =
1 '2 (u x + u y'2 + uz'2) 2
ui′ = ui − u¯i
(1) (2)
where u¯i is the mean velocity at the point during the sampling period, ui is local velocity, namely instantaneous velocity, and ui′ is the fluctuating component of velocity at sampling time t. u x′ , u y′ , uz′ are metrics of fluctuating velocity in three different directions of x, y, z; the units all are m/s. TDR is associated with flow that characterizes the state of turbulence (Xu and Chen, 2013), which has been quantified in studies fish movement (Gao et al., 2016), which can be simplified as:
2. Material and methods 2.1. Experimental setup Experiments were conducted in a 1:2.5 scale VSF physical model installed at the Engineering Research Center of Eco-Environment in Three Gorges reservoir region, Yichang (China). The structure was 7 m long and 0.7 m high, and externally reinforced by glass sidewalls (Fig. 1). The VSF consisted of five pools with length L = 0.625 m, height H = 0.7 m and width B = 0.5 m. The width of the baffles were b1 = 0.25 m (b1 = 0.5B) and b2 = 0.125 m (b2 = 0.25B), which were staggered arrangement on opposite sides of the sidewall, respectively. The fishway was set at a slope of 1%. All the head drops were the same (0.00625 m, very small). The water depth in all the pools was similar. The inlet reach of fishway was 2.375 m long, located at the upstream of the flume. A concrete head tank with the length of 2.0 m, the height of 1.5 m and width 1.2 m located at the upstream of inlet reach of fishway, supplied relatively uniform flow into the flume. Flow rates were regulated by an electronically controlled centrifugal pump, which recirculated fishway outflow back to the head tank. An acclimation zone (0.7 m × 0.5 × 0.7 m) was constructed 0.8 m downstream of the first
TDR = Cμ0.75 TKE1.5/ l
(3)
where Cμ is the specified empirical constant in turbulent model (approximately 0.09), and l is turbulence length scale (unit is m), calculated as:
L = 0.07 L
(4)
where L is characteristic length of flow field, approximately equal to the hydraulic diameter of flow field (unit is m). In addition, strain rate (SR) is a quantity measuring the degree of twisting, stretching, compression, and other forms of deformation on an element of water (Goodwin et al., 2006), which is defined as:
∂uj ⎞ ∂u SRij = ⎜⎛ i + ⎟ /2 ∂x i ⎠ ∂ x ⎝ j
(5)
To understand the flow field in different horizontal planes, the 364
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Fig. 1. (a) planform of the experimental VSF; (b) detailed view of pool 3; (c) array of measurement points in each horizontal plane (Note: L, B is the length and width of the pool in fishway, respectively).
111°18′26.04″E). All the fish were measured for the total length and kept in a 1.8 m (diameter) × 0.3 m (height) × 0.2 m (depth of water) aerated fish tank. To recover from transport and handling stress, fish were kept in the tanks for at least 5 days before being tested. The fish were fed on pond sticks (Tetra Gmbh) until 24 h prior to experimentation. Constant recirculation water exchange was used to stabilize water temperature (mean ± SD = 24.2 ± 0.6 °C). At the beginning of a trial, one fish was randomly selected from the tank, and introduced into acclimation zone before experimentation. Two mesh panels were removed (Fig. 1a) to allow the fish to volitionally ascend once the trial started (Table 1). When the fish ascended into pool 5, the test was ended. If the fish could not ascend to pool 5 within 1 h, the trial was ended. Each fish was used only once to avoid possible bias in the outcome of the experiments (Mallen-Cooper, 1994). Experiments were conducted under the two hydraulic conditions (Q = 18.0L/s and 26.0 m/s) with 15 and 18 fish, respectively (Table 1). Total number of trials was 33. After each trial, the total body length (cm) and weight (g) of the fish was measured. The movement of fish was continuously recorded by a video recording system and direct observation through the glass sidewalls of VSF. During the whole process of a trial, direct observations were carried out at approximately 0.5 m distance from the sidewall, and there was no disturbance to fish with approaching and leaving discreetly on the observation points. A video recording system was made up of a digital video recorder (DS-7808N-K1/C, Hikvision Corporation) and a 25fps video camera (DS-2CD3345-I, Hikvision Corporation), which was placed 5 m above the water surface to monitor movement of a single test fish through pools 1–5. Based on direct observation and video monitoring, fish moved preferentially close to the bottom of fishway. Therefore, it was hypothesized that the trajectories of fish varied mainly in the horizontal plane with z = 0.3 h. In order to aid video analysis and quantify fine-scale movement, a reference grid including 25 cells with a length of 3.125 m and width of 0.500 m (contiguous sequentially numbered cells each, 0.125 m length × 0.100 m width) was established in pools 1–5 (Fig. 2). Logger Pro software (Vernier Software & Technology) was used to capture
Table 1 Details of experimental conditions: flow discharge (Q), pool mean depth (h). Total length and weight statistics of fish used in the experiments are also shown. Q(L/s)
h(m)
N
total length/ (cm)
weight/(g)
18 26
0.3 0.4
15 18
10.79 ± 0.55 10.95 ± 0.43
16.80 ± 2.55 17.93 ± 2.25
dimensionless term κ was formulated; κv, κTKE, κTDR, κSR were using the following equations:
κ v = V0/Vm
(6)
κTKE = TKE0 /TKEm
(7)
κTDR = TDR 0/TDRm
(8)
κ SR = SR 0/SRm
(9)
where, V0, KTE0,TDR0, SR0 represent maximum velocity, maximum turbulent kinetic energy, maximum turbulent dissipation rate, maximum strain rate in pool, respectively; while Vm, TKEm, TDRm, SRm represent maximum V, maximum TKE, maximum TDR, maximum SR in the slot area, respectively. 2.3. The experimental fish All experimental bighead carps (N = 33) with the total length range of 10–15 cm (the detail description of experimental bighead carps were also shown in Table 1), were captured by net fishing from the Yidu hatchery during the migratory season. All the individuals were collected and trials were maintained at water temperatures (18–27 °C) representative of those found in the Yangtze River during bighead carp migrations, and within the preferred range for bighead carp activity (Jennings, 1988; Newbold et al., 2016). They were transported 1 h in aerated bags to Engineering Research Center of Eco-environment of China, Three Gorges University, Yichang (30°43′47.38″N, 365
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Fig. 2. (a): Reference coordinate grid of VSF for fish movement behavior observation (each cell was spaced at 0.125 m length and 0.1 m width) attached to the bottom of pool 1–5; (b) a detail reference grid in pool 3.
extract data such as fish’s positions, swimming speeds and fish transit times from video camera using frame-by-frame video analysis. Time spent by each fish within each cell was also quantified and further analyzed with respect to hydraulic characteristics.
2.5. Statistical analysis Spearman rank correlation was applied to analyze correlations between hydraulic parameters (TKE, TDR, SR, V) and transit time of fish movement in pools. Analyses were performed using data collected at z = 0.3 h in all experiments, as fish preferentially moved close to the bottom of the fishway based on the video observations and monitoring. The statistical procedure was carried out using IBM SPSS Statistics 23.
2.4. Numerical model The numerical model of VSF consisted of 5 active pools, an inlet reach (2.375 m) and an outlet reach (1.5 m), where L = 0.625 m is the length of a pool (Fig. 1). A robust three-dimensional computational fluid dynamics (CFD) solver FLUENT was utilized to model flow field distribution in the VSF. Therefore, the xy-plane hydraulic distribution can be obtained from the simulation. Computation was performed using a RNG k-ε turbulence model for modeling turbulence through the vertical slots. The detailed continuity equation, continuity momentum equation, RNG k-ε equations can found on Rastogi and Rodi (1978)’ work. The RNG k-ε model constants are the CFD recommended constants as reference on Orszag’s (Orszag, et al., 1993) work. The computational domain for the VSF was defined by a structured mesh with a total of 1,094,634 cells. A velocity-type inlet boundary condition was adopted at the inlet of fishway with velocity value (V = 0.12 m/s with Q = 18.0L/s and V = 0.13 m/s with Q = 26.0L/s) determined by experimental velocity. A pressure-type outlet boundary condition was given at the exit of the VSF. Regular wall boundary conditions were applied all along the walls of the fishway as well as at the baffles. Converged solutions were achieved by running 1300 time steps with a time step of 0.001 s. The numerical simulation values of velocity and turbulent kinetic energy magnitude were compared with the corresponding experimental measured values in the third pool, as shown in Fig. 3. Although the simulation and measured V and TKE were not consistency, there were significance correlation between the measured and the simulated velocity values (R1V2 = 0.880, R2V2 = 0.966) and TKE values (R1T2 = 0.740, R2T2 = 0.749) (Fig. 4). For this reason, the RNG k-ε model was considered to be successfully validated for simulating VSF. Therefore, the calculation hydraulic parameters (V, TKE, TDR, SR) distributions in the VSF was used to combine the fish movement trajectories.
3. Results 3.1. Hydraulics The velocity measurements in points A1, A2 in various depths were shown in Fig. 5. The velocity measurement results revealed that the flow in the observed VSF was indeed two dimensional, for example, the vertical component vz was small while horizontal components vx and vy remained practically constant for all z/h, as shown in Fig. 5. The velocity in the pool 3 in horizontal planes (z = 0.3 h, 0.5 h and 0.7 h) was shown in Fig. 6 with Q = 18.0L/s. In Fig. 6, the velocity vectors indicated that recirculating regions were formed on both sides of the jet zone which entered into a pool through the gap between the staggered arrangement baffles. In the horizontal plane at 0.3 h, two different regions could be clearly distinguished: the jet region, with maximum velocities reaching 0.51 m/s in the short baffles, and in recirculation regions characterized by low velocities. At the horizontal plane 0.7 h, the recirculation regions can be observed with the velocities (0.13–0.27 m/s) of occurring along this plane. Dimensionless κv, κTKE, κTDR, κSR in different horizontal planes (Q = 18.0L/s) were present in Fig. 7. Dimensionless velocities, TDR, SR were maximum in the short slot (x/L = 0.6) (Fig. 7a–c). The dimensionless TKE were relatively higher (around 0.8–1.0) in pool 3 (Fig. 7b). 3.2. The fish movement trajectories In all the experiments, 33 individual fish were tested in the VSF, and 23 fish successfully passed through fishway. The mean passage success of bighead carps for all trials (n = 33) was 69.7%, while the passage success was 80% with Q = 18.0L/s and 61.1% with Q = 26.0L/s. Accordingly, the time took by all the fish to successfully ascend was a mean ± S.D. of (17.08 ± 8.39) s with Q = 18.0L/s. With Q = 26.0L/ 366
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Fig. 3. The comparison between the numerical simulated value and the measured value: (a) and (b): the mean velocity and turbulent kinetic energy along x direction with y = 0.2 m (z = 0.3 h), respectively; (c) and (d): the mean velocity and turbulent kinetic energy along y direction with x = 3.225 m (z = 0.3 h), respectively; (e) and (f): the mean velocity and turbulent kinetic energy at x = 3.275 m, y = 0.2 m in three different horizontal planes.
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Fig. 4. The simulated V, TKE values were compared with the corresponding measured values as shown in Fig. 4. Linear regression (Goodwin et al.; 2014; An et al., 2016) of simulated and measured V values in two different positions (P1: y direction with x = 3.225 m (z = 0.3 h); P2: x direction with y = 0.2 m (z = 0.3 h)) were present in Fig. 4a, respectively. The R1V2, R2V2 were 0.880, 0.966, respectively. Fig. 4b was simulated and measured TKE values in positions P1, P2. The R1T2, R2T2 were 0.740, 0.749 respectively. (Notes: in position (at x = 3.275 m, y = 0.2 m in three different horizontal planes) only have three V and TKE values, so it is not suitable to analysis the correlation.)
this hydraulic distribution. From Figs. 8d and 9d, some fish tended to avoid the area of high strain rate, but other fish swam directly through this area. Fish transit time in each cell for different hydraulic parameters is shown in Fig. 10. Fish spent more time in cells with mean V values ranging from 0.15 to 0.45 m/s in Fig. 10a, and a significant correlation between V and fish transit times was found (Spearman rank correlation: Q = 18.0 m/s, r = 0.397, P < 0.01; Q = 26.0 m/s, r = 0.407, P < 0.01). Fish tended to avoid cells in which TKE exceeded about 0.045 m2/s2, but fish preferred regions with mean TKE in the range between 0.020 and 0.043 m2/s2, and thus avoid areas with the lowest TKE levels in Fig. 10b. A significant correlation was noted between TKE and transit times (Spearman rank correlation: Q = 18.0 m/s, r = 0.476, P < 0.01; Q = 26.0 m/s, r = 0.469, P < 0.01). The preferred range for mean TDR values was 0.020～0.065 m2/s3 as shown in Fig. 10c, but there was no significant correlation between TDR and fish transit times (Spearman rank correlation: Q = 18.0 m/s, r = 0.225, P＞0.01; Q = 26.0 m/s, r = 0.208, P＞0.01). In addition, fish used mainly areas with SR of 2–7 s−1 in Fig. 10d, accordingly, no significant relationship
s, fish took more time with a mean ± SD of (25.16 ± 16.53) s. For correspondingly, the mean swimming speed mean ± SD of bighead carp was 0.405 ± 0.091 m/s, and the maximum swimming speed was 1.83 m/s in all the trials. Figs. 8 and 9 showed the typical movement trajectories of fish combined with four hydraulic parameters (TKE, V, TDR and SR) with Q = 18.0 L/s and Q = 26.0 L/s, respectively. As can be seen from the Figs. 8 and 9, some fish chose a longer route in pool upstream successfully, while others inclined to select a short distance route when passing through pools. For all the ascended successfully individuals, Very few (3 individuals) fall back movements and disorientation were observed, and most of the fish were inclined to follow similar trajectories and exploit the same flow regions in pools. As can be seen from Figs. 8a and 9a, fish passed from one pool into another through the slots, some individuals tended to stay behind the baffles avoid high velocity areas. In Fig. 8b, most of fish continued to pass through pools chose movement paths in order to avoid the excessively high TKE. TDR was attained high levels near the baffles in Figs. 8c and 9c. However, there was no clear relationship between fish movement trajectories and
Fig. 5. Measured instantaneous velocities with Q = 18.0L/s and Q = 26.0L/s showing the two-dimensional nature of the VSF flow. 368
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Fig. 6. The velocity vector distribution with 18.0L/s in pool 3 in three horizontal planes: (a) 0.3 h; (b) 0.5 h; (c) 0.7 h.
in fish’s path selection. Gao et al. (2016) suggested that optimal TKE levels for fish movement might exist. Indeed, fish can be attracted to turbulent flows if their mechanisms of stability are sufficient for a given hydrodynamic environment (Calluaud et al, 2014). Previous research has also established that the fish cannot distinguish direction in pools with excessive turbulence (Tarrade et al., 2011; Enders et al., 2005). In this study, a higher TKE may have caused some disorientation and reduction of swimming performance, as shown by a short fish transit time. Enders et al. (2005) developed a swimming cost model for juvenile Atlantic salmon (Salmo salar) by estimating the total swimming cost, and showed that the increased TKE resulted in increasing total swimming energetic expenditure for fish. Silva et al. (2012) demonstrated that avoiding high turbulence was a common behavior by a potamodromous cyprinid species-barbel, which usually attempted to minimize energy expenditure to sustain stability. Based on our video fish observations, it was hypothesized that stability of fish was altered following the varied flow field. Bighead carp moved within a preferential area upstream, and TKE was a key hydraulic parameter to determine Bighead carp movement trajectories in this experimental VSF. Not surprisingly, the most preferred mean TDR areas for bighead carp was 0.02–0.065 m2/s3, moreover, fish preferred the mean SR ranges of 2–7 s−1. We are inclined to conclude that these ranges may incur less energy cost during fish movement (Pavlov et al., 2000). When
was found for SR and fish transit times (Spearman rank correlation: Q = 18.0 m/s, r = 0.199, P＞0.05; Q = 26.0 m/s, r = 0.250, P＞0.05). 4. Discussion 4.1. The hydraulic preference of bighead carp A preliminary analysis of the fish trajectories revealed that the bighead carp had a longer transit time with mean water velocity range of 0.15–0.45 m/s. We conclude that there was a preference and selectivity of hydraulic parameter velocity for bighead carp when moving in fishway. Actually, the actual movement trajectories of fish in VSFs usually avoid the high velocity zones and use the lower velocity and turbulence levels ascending through slots. Furthermore, a correlation (r = 0.481, P < 0.01) between fish transit time and the mean velocity magnitude near the bottom of fishway (z = 0.3 h) was shown. It implied that the magnitude of mean velocity was one of key hydraulic parameters in the present study. Bighead carp spent more time on areas with TKE 0.02–0.043 m2/s2 in VSF, which was a preferential TKE range for this species. In particular, the correlation was strongest between fish transit time and mean TKE of all the tested hydraulic variables, implying the importance of this variable as a key parameter determining bighead carp’ movements within the VSF. Goettel et al. (2015) implied that TKE might be a factor 369
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Fig. 7. Dimensionless distribution curves in three different horizontal planes with Q = 18.0 L/s in pool 3: (a) V; (b) TKE; (c) TDR; (d) SR.
swimming in turbulent flows, fish were exposed to a complex water environment acting on their non-linear movement behavior such as rotation and translation (Liao, 2007). High TDR and SR may lead to disorientation or even displacement (Lupandin, 2005). Such disorientation may have resulted by a more prominent effect of large turbulence vortex systems on a fish’s body.
behavior and integrate physiological characteristics. In addition, the configuration of the VSF with long baffles and short baffles located on opposite sides of the sidewalls used in the present study promoted a higher percentage (69.7%) for fish upstream passage efficiency, compared to the proportion of 54.2% in a fishway arranged long and short baffles in the same side with Q = 13.5 L/s (Tan et al., 2017). A possible cause for the present fish passage efficiency could be the impact of the higher turbulence observed in the vicinity of the baffles, on fish body surfaces. Therefore, further study and consideration are required to optimize these designs because even higher efficiencies are needed in present fish pass facilities.
4.2. The movement behavior of bighead carp Although the studies cannot fully replicate natural conditions, they do provide the opportunity to for improving understanding fish responses behavior (Santos et al., 2014). In this paper, Bighead carp utilized cooperative and varied swimming strategies when moving upstream through the pools. Movement trajectories combined with video monitoring data showed that fish commonly passed through the areas of low TKE. Actually, the fish appeared to have high transit times in these areas with a lowered TKE. The present study demonstrated that the relatively low TKE areas played an important role on fish trajectories. Considering of the experiment bighead crap was not wild, it is recognised that movement behavior of these individuals may be conservative and could not directly apply into fishway designs. Further research is required to understand more about the wild fish movement
5. Conclusions The effects of flow turbulence and velocity on movement trajectories of bighead carp were studied in an experimental VSF. Hydraulic distributions (V, TKE, TDR, SR) and fish movement trajectories were associated with the preference hydraulic ranges for this species. The preferred ranges of V, TKE, TDR, SR within the fishway were 0.15–0.45 m/s, 0.02–0.043 m2/s2, 0.02–0.065 m2/s3, 2–7 s−1, respectively. For all the mentioned hydraulic parameters, TKE and V were found to be the hydraulic parameter that most strongly affected 370
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Fig. 8. The typical movement trajectories of bighead carp (black dots) combined with four hydraulic parameters with Q = 18.0L/s: (a) V; (b) TKE; (c)TDR; (d) SR. 371
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Fig. 9. The typical movement trajectories of bighead carp (black dots) combined with four hydraulic parameters with Q = 26.0L/s: (a) V; (b) TKE; (c) TDR; (d) SR.
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Fig. 10. Fish transit time in different hydraulic area in each cell, versus: (a) V; (b) TKE; (c) TDR; (d) SR.
movements for this fish species. Nevertheless, other parameters, which were not investigated in this paper, might also influence the fish movement. Future studies should consider ways to make further improvement to the fish passage efficiency for different size-classes bighead carp and other Asian species in a VSF so as to minimize any possible disorientation for fish in the pools. It should be attempted to lessen the higher TKE zones close to the baffles and reduce the size of the recirculation region. A possible approach might be to place additional structural elements (such as placing different density boulders at the bottom of pools) in the pools (Santos et al., 2013, 2014). In summary, the investigation of fish movement behaviour and the associated hydraulic levels (turbulence and velocity) can provide valuable insights for engineers and biologists to improve fishway design and operation management in relation to this fish species.
Bombač, M., Novak, G., Mlačnik, J., Cetina, M., 2015. Extensive field measurements of flow in vertical slot fishway as data for validation of numerical simulations. Ecol. Eng. 84, 476–484. Calluaud, D., Cornu, V., Baran, P., David, L., 2014. Relationship between fish behavior turbulence and unsteady flow in experimental vertical slot fishways. 10th ISE, Trondheim, Norway. Chorda, J., Maubourguet, M.M., Roux, H., Larinier, M., Tarrade, L., David, L., 2010. Twodimensional free surface flow numerical model for vertical slot fishway. J. Hydraul. Res. 48 (2), 141–151. Duan, X., Liu, S., Huang, M., Qiu, S., Li, Z., Wang, K., Chen, D., 2009. Changes in abundance of larvae of the four domestic Chinese carps in the middle reach of the Yangtze River, China, before and after closing of the Three Gorges Dam. Environ. Biol. Fishes 86 (1), 13–22. Enders, E.C., Boisclair, D., Roy, A.G., 2005. A model of total swimming costs in turbulent flow for juvenile Atlantic salmon. Can. J. Fish. Aquat. Sci. 62 (5), 1079–1089. FAO/DVWK, 2002. Fish passes: design, dimensions and monitoring. Food and Agriculture Organization in arrangement with DVWK, 2002. Gao, Z., Andersson, H.I., Dai, H., Jiang, F., Zhao, L., 2016. A new Eulerian-Lagrangian agent method to model fish paths in a vertical slot fishway. Ecol. Eng. 88, 217–225. Goettel, M.T., Atkinson, J.F., Bennett, S.J., 2015. Behavior of western blacknose dace in a turbulence modified flow field. Ecol. Eng. 74, 230–240. Goodwin, R.A., Nestler, J.M., Anderson, J.J., Weber, L.J., Loucks, D.P., 2006. predicting 3-D fish movement behavior using a Eulerian-Lagrangian-agent method(ELAM). Ecol. Model. 192, 197–223. Goodwin, R.A., Politano, M., Garvin, J.W., Nestler, J.M., Hay, D., Anderson, J.J., Weber, L.J., Dimperio, E., Smith, D.L., Timko, M., 2014. Fish navigation of large dams emerges from their modulation of flow field experience. Proc. Natl. Acad. Sci. 111 (14), 5277–5282. Hoover, J.J., Zielinski, D.P., Sorensen, P.W., 2017. Swimming performance of adult bighed carp Hypophthalmichthys nobilis (Richardson, 1845) and silver carp H. molitrix (Valenciennes, 1844). J. Appl. Ichthyol. 33 (1), 54–62. Hu, M., Hua, Q., Zhou, H., Wu, Z., Wu, X., 2015. The effect of dams on the larval abundance and composition of four carp species in key river systems in China. Environ. Biol. Fishes 98 (4), 1201–1205. Jennings, D.P., 1988. Bighead carp (Hypophthalmichthys nobilis): a biologicalsynopsis. U.
Acknowledgements This study was supported by the National Natural Science Foundation of China, China (51709152, 51579136); the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China (China Institute of Water Resources and Hydropower Research) (IWHR-SKL-201616). References An, R., Li, J., Liang, R., Tuo, Y., 2016. Three-dimensional simulation and experimental study for optimising a vertical slot fishway. J. Hydro-Environ. Res. 12, 119–129.
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C., Pinheiro, A.N., 2018a. Passage performance of two cyprinids with different ecological traits in a fishway with distinct vertical slot configurations. Ecol. Eng. 105, 180–188. Sampson, S.J., Chick, J.H., Pegg, M.A., 2009. Diet overlap among two Asian carp and three native fishes in backwater lakes on the Illinois and Mississippi rivers. Biol. Invasions 11 (3), 483–496. Santos, J.M., Branco, P.J., Silva, A.T., Katopodis, C., Pinheiro, A.N., Viseu, T., Ferreira, M.T., 2013. Effect of two flow regimes on the upstream movements of the Iberian barbel (Luciobarbus bocagei) in an experimental pool-type fishway. J. Appl. lchthyol. 29, 425–430. Santos, J.M., Branco, P., Katopodis, C., Ferreira, M.T., Pinheiro, A.N., 2014. Retrofitting pool-and-weir fishways to improve passage performance of benthic fishes: effect of boulder density and fishway discharge. Ecol. Eng. 73, 335–344. Shi, X., Kynard, B., Liu, D., Chen, Q., 2015. Development of fish passage in China. Fisheries 40 (4), 161–169. Silva, A.T., Santos, J.M., Ferreira, M.T., Pinheiro, A.N., Katopodis, C., 2011. Effects of water velocity and turbulence on the behaviour of Iberian barbel (Luciobarbusbocagei, Steindachner 1864) in an experimental pool-type fishway. River Res. Appl. 27 (3), 360–373. Silva, A.T., Katopodis, C., Santos, J.M., Ferreira, M.T., Pinheiro, A.N., 2012. Cyprinid swimming behaviour in response to turbulent flow. Ecol. Eng. 44 (3), 314–328. Tan, J., Dai, H., Gao, Z., 2016. Fish species and hydropower projects in upper Yangtze River in China: impacts and rehabilitation. Fresenius Environ. Bull. 25 (6), 1846–1857. Tan, J., Gao, Z., Dai, H., Shi, X., 2017. The correlation analysis between hydraulic characteristics of vertical slot fishway and fish movement characteristics. J. Hydraul. Eng. 48 (5), 1–10. Tarrade, L., Pineau, G., Calluaud, D., Texier, A., David, L., Larinier, M., 2011. Detailed experimental study of hydrodynamic turbulent flows generated in vertical slot fishways. J. Environ. Fluid Mechan. 11, 1–21. Wittmann, M.E., Jerde, C.L., Howeth, J.G., Maher, S.P., Deines, A.M., Jenkins, J.A., Whittledge, G.W., Burbank, S.R., Chadderton, W.L., Mahoon, A.R., Tyson, J.T., Gantz, C.A., Keller, R.P., Drake, J.M., Lodge, D.M., 2014. Grass carp in the Great Lakes region: establishment potential, expert perceptions, and re-evaluation of experimental evidence of ecological impact. Can. J. Fish. Aquat. Sci. 71 (7), 992–999. Wu, S., Rajaratnam, N., Katopodis, C., 1999. Structure of flow in vertical slot fishway. J. Hydraul. Eng. 125 (4), 351–360. Wu, X., Rao, J., He, B., 1992. The history of the Chinese freshwater fisheries. In: Liu, J., He, B. (Eds.), Cultivation of the Chinese Freshwater Fishes. Science Press, Beijing, pp. 5–29. Xu, D., Chen, J., 2013. Accurate estimate of turbulent dissipation rate using PIV data. Exp. Therm Fluid Sci. 44, 662–672.
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Effects of turbulence and velocity on the movement behaviour of bighead carp (Hypophthalmichthys nobilis) in an experimental vertical slot fishway Junjun Tana, Zhu Gaob, Huichao Daia,c, Zhongyong Yanga, Xiaotao Shia,
T
⁎
a
Engineering Research Center of Eco-environment in Three Gorges Reservoir Region, Ministry of Education, China Three Gorges University, Yichang, Hubei 443002, China College of Transportation, Nantong University, Nantong, Jiangsu 226019, China c College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, Jiangsu 210098, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Bighead carp VSF Hydraulic distribution Fish movement trajectories TKE
The vertical slot fishway (VSF) plays a major role in protection and restoration of migratory fishes at many migratory barriers. However, the VSF design greatly depends on fish response to hydraulics and swimming behavior, which are key to the design of effective VSFs for target fish species. This study evaluated the effects of hydraulic characteristics on movement behavior of bighead carp (Hypophthalmichthys nobilis) by combining spatial hydraulic distributions with fish movement trajectories in an experimental VSF. Fish spent more transit times in ranges of velocity (V) with 0.15–0.45 m/s, turbulent kinetic energy (TKE) with 0.020–0.043 m2/s2, turbulent dissipation rate (TDR) with 0.020–0.065 m2/s3and strain rate (SR) with 2–7 s−1 under different discharges (18.0L/s and 26.0L/s), respectively. Correlation were found between fish transit times and hydraulic variables of velocity (V) (Q = 18.0 m/s: r = 0.397, P < 0.01; Q = 26.0 m/s: r = 0.407, P < 0.01), turbulent kinetic energy (TKE) (Q = 18.0 m/s: r = 0.476, P < 0.01; Q = 26.0 m/s: r = 0.469, P < 0.01). TKE and V were the key hydraulic parameters associated with the fish transit movement times in VSF. The results provided key hydraulic parameters for bighead carp movement in VSFs, which can serve as reference design for the future VSF and others fishway designs for this species.
1. Introduction The natural flow regimes of rivers have been significantly altered by the construction of engineered structures such as dams, weirs and other migratory barriers, which reduced ecological connectivity of river systems, fragmented migration routes, and in many cases caused the rapid decrease in abundances of freshwater fishes, especially on those that complete their migrations within river systems (Poulet, 2007). In order to successfully restore regulated river systems, technical fishways have been developed to enhance upstream passage of fish (Katopodis, 2005; Tan et al., 2016), which can provide a significant role in enhancing river connectivity and effective upstream migration of fish. The VSF is one of the most frequently used technical fishway designs for facilitating fish passage at larger artificial barriers, such as dams, weirs, or sluices (FAO/DVWK, 2002; Shi et al., 2015). The main advantage of the VSF is that the hydraulic characteristics in this design are quasi-independent from the discharge or water depth variation in the fishway (Chorda et al., 2010; Rajaratnam et al., 1992). Research on VSFs has been focused primarily on obtaining suitable hydraulic design and operation criteria for fish passage (Rajaratnam et al., 1992; Wu
⁎
et al., 1999; Puertas et al., 2012; Bombač et al., 2015; An et al., 2016). However, the research and design work is complex, in that it must consider practical hydraulic design criteria based on the ability and willingness of fish to seek and accept the hydrodynamic conditions presented therein (Katopodis and Williams, 2012). Also, advances in biologically-based oriented fishway research (e.g. preferences of fish movement or pathway within a fishway) were including anadromous fish species (especially salmonid species) and potamodromous cyprinids (Romão et al., 2018a,b), etc. Little research has been performed to date on passage of potamodromous Asian fishes (e.g. Asian carps) and is therefore required for effective VSF design for these species. The requirement is highlighted in Yangtze River basin in China where potamodromous Asian fish species are frequently the most abundant fish species. Black carp (Mylopharyngodon piceus), grass carp (Ctenopharyngodon idella), silver carp (H. molitrix) and Bighead carp (Hypophthalmichthys nobilis), are the most commercially important freshwater Asian carp species in China, particularly in the Yangtze River basin. Historically, the catch of these four species in the Yangtze River accounts for 63% of the total natural fish production in all of China (Wu et al., 1992). They
Corresponding author. E-mail address: [email protected] (X. Shi).
https://doi.org/10.1016/j.ecoleng.2018.12.002 Received 10 May 2018; Received in revised form 24 November 2018; Accepted 2 December 2018 0925-8574/ © 2018 Published by Elsevier B.V.
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pool to allow fish to adjust the flume conditions prior to release. Thermostatic laboratory controls and a heater/chiller were used to regulate the experimental water temperature. Free surface elevations in the VSF were measured using a point gauge to ± 0.1 mm.
are typical potamodromous fish, both adults and juveniles have extensive spawning and feeding migrations (Hu et al., 2015). However, the abundance of these carps dropped to just 4.2% of the historic average abundance in 2005 (Duan et al., 2009) by the widespread sluice gate installation for water conservancy projects. It is urgent to provide effective fish passage for these potamodromous fish. In addition, Asian carp have been introduced to many countries for biological control in aquaculture, and have had a negative impact on many native species and ecosystems (Nico et al., 2005; Wittmann et al., 2014; Hoover et al., 2017). Bighead carp was introduced in the 1970s and have been reproducing since the 1980s due to their fast growth rate and efficient filter feeding (Kolar et al., 2005). They have been found to have the potential to outcompete native species such as gizzard shad (Dorosoma cepedianum, Lesueur 1818), bigmouth buffalo (Ictiobus cyprinellus, Valenciennes 1844) (Sampson et al., 2009). In response to this situation, management decided to control and research to prevent further spread of bighead carp, particularly to the Mississippi River (Hoover et al., 2017). Using fish movement behavior to design effective hydraulic structures is a potential method for successful fish passage and reducing the spread of invasive Asian carp (Newbold et al., 2016). Although their swimming ecology has been assessed in recent studies (Newbold et al., 2016), knowledge of these species’ movement behavior response to hydraulic characteristics is sparse. Hydraulic variables including flow turbulence (turbulent kinetic energy [TKE], turbulent dissipation rate [TDR], and strain rate [SR]) (Liao, 2007; Silva et al., 2011; Gao et al, 2016) and velocity (V) are known to be closely related to fish swimming abilities, and affect passage performance (Pavlov et al., 2000; Silva et al., 2011). Therefore, it is necessary to understand how the above hydraulic parameters affect fish movement within fishways and to develop fundamental guidelines for future fishway designs. However, it has proven to be extremely difficult to get accurate measurement of both hydraulic and biological performance parameters in the field (Silva et al., 2011). Controlled laboratory experiments can be conducted on homologous conditions, allow direct observation of fish movement behavior, and also allow the control of potential confounding variables (Santos et al., 2014). The primary objective of this study was to understand the movement behaviour of one of the four Asian carp species – bighead carp – in response to four hydrodynamic indicators (V, TKE, TDR, SR) under controlled conditions in an experimental VSF. Specific questions to be addressed include: 1) which hydraulic ranges were fish preferences relate to fish movement trajectories and transit time within fishway? and 2) Which were the key hydraulic parameters that most significantly affected passage performance in VSFs for this species?
2.2. Hydraulics Experimental conditions were given in Table 1. Detailed instantaneous velocity measurements were monitored using a SonTek 16 MHz Micro acoustic Doppler Velocimeter (ADV) oriented vertically down, to measure the three-dimensional velocity components (x, y, z). The probe was positioned in the flow with an adjustable traverse and caused negligible disturbance to the observed flow. Sampling frequency of the ADV was 50 Hz for a sampling period of 60 s for each point. Velocity measurements were taken in the third pool downstream of the fishway exit because flow was fully established in this pool and velocity conditions at the entrance and the exit of this pool were similar. Measurements were taken at three horizontal planes parallel to the flume bottom, namely z = 0.3 h, 0.5 h and 0.7 h (h was the pool mean depth). In detailed, 0.3 h (9 cm), 0.5 h (15 cm), 0.7 h (21 cm) for 18.0L/s and 0.3 h (12 cm), 0.5 h (20 cm), 0.7 h (28 cm) for 26.0L/s were considered for measurements. Flow velocities were measured in a total of 102 points in each horizontal plane located 0.025–0.05 m apart (Fig. 1c). In addition, velocity at the relative depth (0.4 h, 0.6 h, 0.8 h), which parallel to the flume bottom, was measured at two given points A1, A2 under two flow conditions (Fig. 1c). So in measurement points A1 and A2 totally have measured six relative depths (0.3 h, 0.4 h, 0.5 h, 0.6 h, 0.7 h and 0.8 h). Totally, 312 instantaneous measurement points were recorded. Turbulence is one of the major variables that influences the performance of fish movement behavior (Lupandin, 2005). And it is the time and spatially dependent heterogeneous motion of rapid flow velocity fluctuations coming from the multiple chaotic water flow (Liu et al., 2006). TKE, TDR and SR are metrics that can be used to define the effect of turbulence on fish (Silva et al., 2011; Gao et al., 2016) that can be calculated based on the instantaneous velocity. TKE was related velocity fluctuation at a given point, and defined as:
TKE =
1 '2 (u x + u y'2 + uz'2) 2
ui′ = ui − u¯i
(1) (2)
where u¯i is the mean velocity at the point during the sampling period, ui is local velocity, namely instantaneous velocity, and ui′ is the fluctuating component of velocity at sampling time t. u x′ , u y′ , uz′ are metrics of fluctuating velocity in three different directions of x, y, z; the units all are m/s. TDR is associated with flow that characterizes the state of turbulence (Xu and Chen, 2013), which has been quantified in studies fish movement (Gao et al., 2016), which can be simplified as:
2. Material and methods 2.1. Experimental setup Experiments were conducted in a 1:2.5 scale VSF physical model installed at the Engineering Research Center of Eco-Environment in Three Gorges reservoir region, Yichang (China). The structure was 7 m long and 0.7 m high, and externally reinforced by glass sidewalls (Fig. 1). The VSF consisted of five pools with length L = 0.625 m, height H = 0.7 m and width B = 0.5 m. The width of the baffles were b1 = 0.25 m (b1 = 0.5B) and b2 = 0.125 m (b2 = 0.25B), which were staggered arrangement on opposite sides of the sidewall, respectively. The fishway was set at a slope of 1%. All the head drops were the same (0.00625 m, very small). The water depth in all the pools was similar. The inlet reach of fishway was 2.375 m long, located at the upstream of the flume. A concrete head tank with the length of 2.0 m, the height of 1.5 m and width 1.2 m located at the upstream of inlet reach of fishway, supplied relatively uniform flow into the flume. Flow rates were regulated by an electronically controlled centrifugal pump, which recirculated fishway outflow back to the head tank. An acclimation zone (0.7 m × 0.5 × 0.7 m) was constructed 0.8 m downstream of the first
TDR = Cμ0.75 TKE1.5/ l
(3)
where Cμ is the specified empirical constant in turbulent model (approximately 0.09), and l is turbulence length scale (unit is m), calculated as:
L = 0.07 L
(4)
where L is characteristic length of flow field, approximately equal to the hydraulic diameter of flow field (unit is m). In addition, strain rate (SR) is a quantity measuring the degree of twisting, stretching, compression, and other forms of deformation on an element of water (Goodwin et al., 2006), which is defined as:
∂uj ⎞ ∂u SRij = ⎜⎛ i + ⎟ /2 ∂x i ⎠ ∂ x ⎝ j
(5)
To understand the flow field in different horizontal planes, the 364
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Fig. 1. (a) planform of the experimental VSF; (b) detailed view of pool 3; (c) array of measurement points in each horizontal plane (Note: L, B is the length and width of the pool in fishway, respectively).
111°18′26.04″E). All the fish were measured for the total length and kept in a 1.8 m (diameter) × 0.3 m (height) × 0.2 m (depth of water) aerated fish tank. To recover from transport and handling stress, fish were kept in the tanks for at least 5 days before being tested. The fish were fed on pond sticks (Tetra Gmbh) until 24 h prior to experimentation. Constant recirculation water exchange was used to stabilize water temperature (mean ± SD = 24.2 ± 0.6 °C). At the beginning of a trial, one fish was randomly selected from the tank, and introduced into acclimation zone before experimentation. Two mesh panels were removed (Fig. 1a) to allow the fish to volitionally ascend once the trial started (Table 1). When the fish ascended into pool 5, the test was ended. If the fish could not ascend to pool 5 within 1 h, the trial was ended. Each fish was used only once to avoid possible bias in the outcome of the experiments (Mallen-Cooper, 1994). Experiments were conducted under the two hydraulic conditions (Q = 18.0L/s and 26.0 m/s) with 15 and 18 fish, respectively (Table 1). Total number of trials was 33. After each trial, the total body length (cm) and weight (g) of the fish was measured. The movement of fish was continuously recorded by a video recording system and direct observation through the glass sidewalls of VSF. During the whole process of a trial, direct observations were carried out at approximately 0.5 m distance from the sidewall, and there was no disturbance to fish with approaching and leaving discreetly on the observation points. A video recording system was made up of a digital video recorder (DS-7808N-K1/C, Hikvision Corporation) and a 25fps video camera (DS-2CD3345-I, Hikvision Corporation), which was placed 5 m above the water surface to monitor movement of a single test fish through pools 1–5. Based on direct observation and video monitoring, fish moved preferentially close to the bottom of fishway. Therefore, it was hypothesized that the trajectories of fish varied mainly in the horizontal plane with z = 0.3 h. In order to aid video analysis and quantify fine-scale movement, a reference grid including 25 cells with a length of 3.125 m and width of 0.500 m (contiguous sequentially numbered cells each, 0.125 m length × 0.100 m width) was established in pools 1–5 (Fig. 2). Logger Pro software (Vernier Software & Technology) was used to capture
Table 1 Details of experimental conditions: flow discharge (Q), pool mean depth (h). Total length and weight statistics of fish used in the experiments are also shown. Q(L/s)
h(m)
N
total length/ (cm)
weight/(g)
18 26
0.3 0.4
15 18
10.79 ± 0.55 10.95 ± 0.43
16.80 ± 2.55 17.93 ± 2.25
dimensionless term κ was formulated; κv, κTKE, κTDR, κSR were using the following equations:
κ v = V0/Vm
(6)
κTKE = TKE0 /TKEm
(7)
κTDR = TDR 0/TDRm
(8)
κ SR = SR 0/SRm
(9)
where, V0, KTE0,TDR0, SR0 represent maximum velocity, maximum turbulent kinetic energy, maximum turbulent dissipation rate, maximum strain rate in pool, respectively; while Vm, TKEm, TDRm, SRm represent maximum V, maximum TKE, maximum TDR, maximum SR in the slot area, respectively. 2.3. The experimental fish All experimental bighead carps (N = 33) with the total length range of 10–15 cm (the detail description of experimental bighead carps were also shown in Table 1), were captured by net fishing from the Yidu hatchery during the migratory season. All the individuals were collected and trials were maintained at water temperatures (18–27 °C) representative of those found in the Yangtze River during bighead carp migrations, and within the preferred range for bighead carp activity (Jennings, 1988; Newbold et al., 2016). They were transported 1 h in aerated bags to Engineering Research Center of Eco-environment of China, Three Gorges University, Yichang (30°43′47.38″N, 365
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Fig. 2. (a): Reference coordinate grid of VSF for fish movement behavior observation (each cell was spaced at 0.125 m length and 0.1 m width) attached to the bottom of pool 1–5; (b) a detail reference grid in pool 3.
extract data such as fish’s positions, swimming speeds and fish transit times from video camera using frame-by-frame video analysis. Time spent by each fish within each cell was also quantified and further analyzed with respect to hydraulic characteristics.
2.5. Statistical analysis Spearman rank correlation was applied to analyze correlations between hydraulic parameters (TKE, TDR, SR, V) and transit time of fish movement in pools. Analyses were performed using data collected at z = 0.3 h in all experiments, as fish preferentially moved close to the bottom of the fishway based on the video observations and monitoring. The statistical procedure was carried out using IBM SPSS Statistics 23.
2.4. Numerical model The numerical model of VSF consisted of 5 active pools, an inlet reach (2.375 m) and an outlet reach (1.5 m), where L = 0.625 m is the length of a pool (Fig. 1). A robust three-dimensional computational fluid dynamics (CFD) solver FLUENT was utilized to model flow field distribution in the VSF. Therefore, the xy-plane hydraulic distribution can be obtained from the simulation. Computation was performed using a RNG k-ε turbulence model for modeling turbulence through the vertical slots. The detailed continuity equation, continuity momentum equation, RNG k-ε equations can found on Rastogi and Rodi (1978)’ work. The RNG k-ε model constants are the CFD recommended constants as reference on Orszag’s (Orszag, et al., 1993) work. The computational domain for the VSF was defined by a structured mesh with a total of 1,094,634 cells. A velocity-type inlet boundary condition was adopted at the inlet of fishway with velocity value (V = 0.12 m/s with Q = 18.0L/s and V = 0.13 m/s with Q = 26.0L/s) determined by experimental velocity. A pressure-type outlet boundary condition was given at the exit of the VSF. Regular wall boundary conditions were applied all along the walls of the fishway as well as at the baffles. Converged solutions were achieved by running 1300 time steps with a time step of 0.001 s. The numerical simulation values of velocity and turbulent kinetic energy magnitude were compared with the corresponding experimental measured values in the third pool, as shown in Fig. 3. Although the simulation and measured V and TKE were not consistency, there were significance correlation between the measured and the simulated velocity values (R1V2 = 0.880, R2V2 = 0.966) and TKE values (R1T2 = 0.740, R2T2 = 0.749) (Fig. 4). For this reason, the RNG k-ε model was considered to be successfully validated for simulating VSF. Therefore, the calculation hydraulic parameters (V, TKE, TDR, SR) distributions in the VSF was used to combine the fish movement trajectories.
3. Results 3.1. Hydraulics The velocity measurements in points A1, A2 in various depths were shown in Fig. 5. The velocity measurement results revealed that the flow in the observed VSF was indeed two dimensional, for example, the vertical component vz was small while horizontal components vx and vy remained practically constant for all z/h, as shown in Fig. 5. The velocity in the pool 3 in horizontal planes (z = 0.3 h, 0.5 h and 0.7 h) was shown in Fig. 6 with Q = 18.0L/s. In Fig. 6, the velocity vectors indicated that recirculating regions were formed on both sides of the jet zone which entered into a pool through the gap between the staggered arrangement baffles. In the horizontal plane at 0.3 h, two different regions could be clearly distinguished: the jet region, with maximum velocities reaching 0.51 m/s in the short baffles, and in recirculation regions characterized by low velocities. At the horizontal plane 0.7 h, the recirculation regions can be observed with the velocities (0.13–0.27 m/s) of occurring along this plane. Dimensionless κv, κTKE, κTDR, κSR in different horizontal planes (Q = 18.0L/s) were present in Fig. 7. Dimensionless velocities, TDR, SR were maximum in the short slot (x/L = 0.6) (Fig. 7a–c). The dimensionless TKE were relatively higher (around 0.8–1.0) in pool 3 (Fig. 7b). 3.2. The fish movement trajectories In all the experiments, 33 individual fish were tested in the VSF, and 23 fish successfully passed through fishway. The mean passage success of bighead carps for all trials (n = 33) was 69.7%, while the passage success was 80% with Q = 18.0L/s and 61.1% with Q = 26.0L/s. Accordingly, the time took by all the fish to successfully ascend was a mean ± S.D. of (17.08 ± 8.39) s with Q = 18.0L/s. With Q = 26.0L/ 366
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Fig. 3. The comparison between the numerical simulated value and the measured value: (a) and (b): the mean velocity and turbulent kinetic energy along x direction with y = 0.2 m (z = 0.3 h), respectively; (c) and (d): the mean velocity and turbulent kinetic energy along y direction with x = 3.225 m (z = 0.3 h), respectively; (e) and (f): the mean velocity and turbulent kinetic energy at x = 3.275 m, y = 0.2 m in three different horizontal planes.
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Fig. 4. The simulated V, TKE values were compared with the corresponding measured values as shown in Fig. 4. Linear regression (Goodwin et al.; 2014; An et al., 2016) of simulated and measured V values in two different positions (P1: y direction with x = 3.225 m (z = 0.3 h); P2: x direction with y = 0.2 m (z = 0.3 h)) were present in Fig. 4a, respectively. The R1V2, R2V2 were 0.880, 0.966, respectively. Fig. 4b was simulated and measured TKE values in positions P1, P2. The R1T2, R2T2 were 0.740, 0.749 respectively. (Notes: in position (at x = 3.275 m, y = 0.2 m in three different horizontal planes) only have three V and TKE values, so it is not suitable to analysis the correlation.)
this hydraulic distribution. From Figs. 8d and 9d, some fish tended to avoid the area of high strain rate, but other fish swam directly through this area. Fish transit time in each cell for different hydraulic parameters is shown in Fig. 10. Fish spent more time in cells with mean V values ranging from 0.15 to 0.45 m/s in Fig. 10a, and a significant correlation between V and fish transit times was found (Spearman rank correlation: Q = 18.0 m/s, r = 0.397, P < 0.01; Q = 26.0 m/s, r = 0.407, P < 0.01). Fish tended to avoid cells in which TKE exceeded about 0.045 m2/s2, but fish preferred regions with mean TKE in the range between 0.020 and 0.043 m2/s2, and thus avoid areas with the lowest TKE levels in Fig. 10b. A significant correlation was noted between TKE and transit times (Spearman rank correlation: Q = 18.0 m/s, r = 0.476, P < 0.01; Q = 26.0 m/s, r = 0.469, P < 0.01). The preferred range for mean TDR values was 0.020～0.065 m2/s3 as shown in Fig. 10c, but there was no significant correlation between TDR and fish transit times (Spearman rank correlation: Q = 18.0 m/s, r = 0.225, P＞0.01; Q = 26.0 m/s, r = 0.208, P＞0.01). In addition, fish used mainly areas with SR of 2–7 s−1 in Fig. 10d, accordingly, no significant relationship
s, fish took more time with a mean ± SD of (25.16 ± 16.53) s. For correspondingly, the mean swimming speed mean ± SD of bighead carp was 0.405 ± 0.091 m/s, and the maximum swimming speed was 1.83 m/s in all the trials. Figs. 8 and 9 showed the typical movement trajectories of fish combined with four hydraulic parameters (TKE, V, TDR and SR) with Q = 18.0 L/s and Q = 26.0 L/s, respectively. As can be seen from the Figs. 8 and 9, some fish chose a longer route in pool upstream successfully, while others inclined to select a short distance route when passing through pools. For all the ascended successfully individuals, Very few (3 individuals) fall back movements and disorientation were observed, and most of the fish were inclined to follow similar trajectories and exploit the same flow regions in pools. As can be seen from Figs. 8a and 9a, fish passed from one pool into another through the slots, some individuals tended to stay behind the baffles avoid high velocity areas. In Fig. 8b, most of fish continued to pass through pools chose movement paths in order to avoid the excessively high TKE. TDR was attained high levels near the baffles in Figs. 8c and 9c. However, there was no clear relationship between fish movement trajectories and
Fig. 5. Measured instantaneous velocities with Q = 18.0L/s and Q = 26.0L/s showing the two-dimensional nature of the VSF flow. 368
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Fig. 6. The velocity vector distribution with 18.0L/s in pool 3 in three horizontal planes: (a) 0.3 h; (b) 0.5 h; (c) 0.7 h.
in fish’s path selection. Gao et al. (2016) suggested that optimal TKE levels for fish movement might exist. Indeed, fish can be attracted to turbulent flows if their mechanisms of stability are sufficient for a given hydrodynamic environment (Calluaud et al, 2014). Previous research has also established that the fish cannot distinguish direction in pools with excessive turbulence (Tarrade et al., 2011; Enders et al., 2005). In this study, a higher TKE may have caused some disorientation and reduction of swimming performance, as shown by a short fish transit time. Enders et al. (2005) developed a swimming cost model for juvenile Atlantic salmon (Salmo salar) by estimating the total swimming cost, and showed that the increased TKE resulted in increasing total swimming energetic expenditure for fish. Silva et al. (2012) demonstrated that avoiding high turbulence was a common behavior by a potamodromous cyprinid species-barbel, which usually attempted to minimize energy expenditure to sustain stability. Based on our video fish observations, it was hypothesized that stability of fish was altered following the varied flow field. Bighead carp moved within a preferential area upstream, and TKE was a key hydraulic parameter to determine Bighead carp movement trajectories in this experimental VSF. Not surprisingly, the most preferred mean TDR areas for bighead carp was 0.02–0.065 m2/s3, moreover, fish preferred the mean SR ranges of 2–7 s−1. We are inclined to conclude that these ranges may incur less energy cost during fish movement (Pavlov et al., 2000). When
was found for SR and fish transit times (Spearman rank correlation: Q = 18.0 m/s, r = 0.199, P＞0.05; Q = 26.0 m/s, r = 0.250, P＞0.05). 4. Discussion 4.1. The hydraulic preference of bighead carp A preliminary analysis of the fish trajectories revealed that the bighead carp had a longer transit time with mean water velocity range of 0.15–0.45 m/s. We conclude that there was a preference and selectivity of hydraulic parameter velocity for bighead carp when moving in fishway. Actually, the actual movement trajectories of fish in VSFs usually avoid the high velocity zones and use the lower velocity and turbulence levels ascending through slots. Furthermore, a correlation (r = 0.481, P < 0.01) between fish transit time and the mean velocity magnitude near the bottom of fishway (z = 0.3 h) was shown. It implied that the magnitude of mean velocity was one of key hydraulic parameters in the present study. Bighead carp spent more time on areas with TKE 0.02–0.043 m2/s2 in VSF, which was a preferential TKE range for this species. In particular, the correlation was strongest between fish transit time and mean TKE of all the tested hydraulic variables, implying the importance of this variable as a key parameter determining bighead carp’ movements within the VSF. Goettel et al. (2015) implied that TKE might be a factor 369
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Fig. 7. Dimensionless distribution curves in three different horizontal planes with Q = 18.0 L/s in pool 3: (a) V; (b) TKE; (c) TDR; (d) SR.
swimming in turbulent flows, fish were exposed to a complex water environment acting on their non-linear movement behavior such as rotation and translation (Liao, 2007). High TDR and SR may lead to disorientation or even displacement (Lupandin, 2005). Such disorientation may have resulted by a more prominent effect of large turbulence vortex systems on a fish’s body.
behavior and integrate physiological characteristics. In addition, the configuration of the VSF with long baffles and short baffles located on opposite sides of the sidewalls used in the present study promoted a higher percentage (69.7%) for fish upstream passage efficiency, compared to the proportion of 54.2% in a fishway arranged long and short baffles in the same side with Q = 13.5 L/s (Tan et al., 2017). A possible cause for the present fish passage efficiency could be the impact of the higher turbulence observed in the vicinity of the baffles, on fish body surfaces. Therefore, further study and consideration are required to optimize these designs because even higher efficiencies are needed in present fish pass facilities.
4.2. The movement behavior of bighead carp Although the studies cannot fully replicate natural conditions, they do provide the opportunity to for improving understanding fish responses behavior (Santos et al., 2014). In this paper, Bighead carp utilized cooperative and varied swimming strategies when moving upstream through the pools. Movement trajectories combined with video monitoring data showed that fish commonly passed through the areas of low TKE. Actually, the fish appeared to have high transit times in these areas with a lowered TKE. The present study demonstrated that the relatively low TKE areas played an important role on fish trajectories. Considering of the experiment bighead crap was not wild, it is recognised that movement behavior of these individuals may be conservative and could not directly apply into fishway designs. Further research is required to understand more about the wild fish movement
5. Conclusions The effects of flow turbulence and velocity on movement trajectories of bighead carp were studied in an experimental VSF. Hydraulic distributions (V, TKE, TDR, SR) and fish movement trajectories were associated with the preference hydraulic ranges for this species. The preferred ranges of V, TKE, TDR, SR within the fishway were 0.15–0.45 m/s, 0.02–0.043 m2/s2, 0.02–0.065 m2/s3, 2–7 s−1, respectively. For all the mentioned hydraulic parameters, TKE and V were found to be the hydraulic parameter that most strongly affected 370
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Fig. 8. The typical movement trajectories of bighead carp (black dots) combined with four hydraulic parameters with Q = 18.0L/s: (a) V; (b) TKE; (c)TDR; (d) SR. 371
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Fig. 9. The typical movement trajectories of bighead carp (black dots) combined with four hydraulic parameters with Q = 26.0L/s: (a) V; (b) TKE; (c) TDR; (d) SR.
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Fig. 10. Fish transit time in different hydraulic area in each cell, versus: (a) V; (b) TKE; (c) TDR; (d) SR.
movements for this fish species. Nevertheless, other parameters, which were not investigated in this paper, might also influence the fish movement. Future studies should consider ways to make further improvement to the fish passage efficiency for different size-classes bighead carp and other Asian species in a VSF so as to minimize any possible disorientation for fish in the pools. It should be attempted to lessen the higher TKE zones close to the baffles and reduce the size of the recirculation region. A possible approach might be to place additional structural elements (such as placing different density boulders at the bottom of pools) in the pools (Santos et al., 2013, 2014). In summary, the investigation of fish movement behaviour and the associated hydraulic levels (turbulence and velocity) can provide valuable insights for engineers and biologists to improve fishway design and operation management in relation to this fish species.
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