GEOG 566






         Advanced spatial statistics and GIScience

April 4, 2018

The Hydraulic and Behavioral Impacts of a Floating Guidance Structure

Filed under: My Spatial Problem @ 1:36 pm

Research question:

Floating guidance structures (also called floating booms or guide walls) are long, partially-submerged panels that alter channel hydraulics to promote safe passage through man-made barriers (Schilt 2007). Their use and implementation is widespread, from the mouths of floating surface collectors to diversion channels and dam forebays (Scott 2014; Reeves et al 2016; Johnson et al 2001). However, their ability to reduce residence times and rates of turbine passage, divert individuals towards surface flow outlets, and ultimately improve passage survival at dams is highly dependent on site-specific characteristics of design and location (Johnson and Dauble 2006; Faber et al 2010; Johnson et al 2001). Until recently, the ability of an engineered structure to guide fish to safe passage has been largely tested either 1) after large-scale implementation in existing reservoirs or 2) in laboratory studies without live subjects (Johnson et al 2001; Scott 2014; Kock et al 2012; Mulligan et al 2017). This research investigates the hydraulic and behavioral impacts of a floating guidance structure in an experimental channel on juvenile Chinook salmon (Oncorhynchus tshawytscha), with the goal of informing the design of guidance structures for more efficient, effective, and safe downstream fish passage of anadromous species at man-made structures.

Description of dataset:

This dataset contains both hydraulic and behavioral information. High resolution tracks of individual fish as they encountered a floating guidance structure at 3 angles of deployment were obtained using the post-processing software, VidSync. Each of 60 trials tracks up to 5 individuals at sub-second intervals with spatial resolution of roughly 5 cm. Observations are constrained to the experimental section of the channel, which is 1.22 m wide, 0.61 m deep, and approximately 2 m long. Hydraulic measurements were interpolated from high-resolution, 3-dimensional velocity measurements at 7 cross-sections throughout the channel onto a hydraulic mesh with <1 cm resolution. Average velocity magnitude (m/s), velocity gradient (m/s/m), acceleration (m/s2), turbulent kinetic energy (TKE, m2/s2), and TKE gradient (m2/s2/m) were calculated for each corner in the hydraulic mesh. By merging these data, the hydraulics encountered by an individual at every point in its track are available.

Previous studies and hypotheses:

Previous flume studies analyzed hydraulic thresholds that incited behavioral change points in fish. Change points included changes in rheotaxis (swimming with head pointed downstream to swimming with head pointed upstream) or in tailbeat frequency. Hydraulic thresholds were discovered at a spatial velocity gradient near 1 cm/s/cm (Enders et al 2012; Vowles and Kemp 2012). Using our dataset, a change in velocity from downstream to upstream is analogous to a change in rheotaxis. Furthermore, we assume that accelerations greater than 2 standard deviations from the mean imply halting behavior (in the case of upstream acceleration) or increase in tailbeat frequency (in the case of downstream acceleration). First, we hypothesize that a hydraulic threshold exists in either spatial velocity gradient, TKE, or TKE gradient for these three behavioral changes. Second, we hypothesize that fish that do not present any of the above behavioral changes follow general rules of fish migration observed in nature: aversion to areas of accelerating or decelerating flow (Haro et al 1998), and attraction to high velocity and TKE (Coutant 1998). The experimental setup can be seen in Figure 1.

Figure 1. Image capture of video recording fish behavior in response to a floating guidance structure deployed at 40 degrees to the flow (seen on the left). Flow is from right to left – these fish are displaying positive rheotaxis.

Approach for analysis, expected outcomes, and preparation:

Proper analyses of fish tracks over time and space using Python will be essential to test our hypotheses. First, behavioral change points must be drawn from complex fish tracks that imply true behavior changes rather than subtle changes in trajectory. The end goal of the behavioral change point analysis contains two parts: 1) a series of graphs like the preliminary data shown in Figure 2, which detail the location of a change point in the channel and the hydraulics at that location, and 2) a statistical test investigating whether the hydraulics at change points between boom angles are significantly different from one another. We hypothesize that they will not; instead, a hydraulic threshold may govern the location of change points, as possibly seen at TKE equal to 10-4 m2/s2 (Figure 2). Second, fish tracks that show no behavioral change points will be investigated for their adherence to general rules of migration. This will be achieved using Arc-Info as a 3-dimensional time-series animation overlaid on the hydraulic mesh. This visualization will confirm whether fish adhere to general migration rules despite the setting of an experimental channel. I am confident in my abilities to complete these analyses using Python; however, I have a lot to learn about Arc-Info and its capabilities.

Figure 2. Preliminary data at 20 and 30 degree deployment angles of fish tracks that show behavioral change points, or halting points. Up arrows upstream behavioral changes (deceleration or upstream movement); down arrows imply downstream behavioral changes (acceleration). Colors differentiate 20 and 30 degree guidance structures.

Significance:

Pacific salmon are a keystone species in both marine and riverine ecosystems. Their nutrient-rich bodies serve as a valuable food resource for many species, from humans to orca whales. Furthermore, Pacific salmon form a cornerstone of the West Coast’s industry, recreation, and culture. However, due to habitat loss, agriculture, logging, overfishing, negative interactions with invasive and hatchery-bred fish, and inadequate flows and passage through impounded rivers, native salmon populations are on the decline (Nehlsen et al 1991). For native stocks to exist in the future, new approaches to fisheries management should be explored. In this research, the impact of floating guidance structures on juvenile Chinook salmon are tested in an experimental channel, with the hope of reducing the negative impacts of dams on Pacific salmon migration and ensuring their survival for future generations.

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2 Comments

  1.   jonesju — April 9, 2018 @ 8:27 am    

    hi Sam,
    Thanks for this blog post. Please insert headings in your blog post that correspond with the questions in the spatial problem blog post assignment.

    As I understand it, your dependent variable is the fish swimming track. A spatial analysis would be based on the actual swimming tracks; therefore I would suggest that you initially create a GIS layer that shows the tracks of each fish. Each track would be represented as a set of segments that correspond to a given time interval. You will need to decide what that time interval should be (a few seconds). Each segment of a track would then be attributed with information about what the fish was doing (e.g., direction, number of tailbeats). I suggest that you create a layer of this type for a few fish, and then we will look at what kind of information you can extract from this.

    •   swanssam — April 11, 2018 @ 9:29 am    

      Thanks for the comments, Julia. I updated the post to include headings, and I’m making headway on a behavioral analysis of fish tracks.

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