Does variable channel morphology lead to dynamic salmon habitat?

Abstract

Stream channel morphology forms the template upon which hydraulic aspects of aquatic habitat are created, yet spatial and temporal variability in habitat imposed by changing morphology is not well understood. This paper presents a conceptual model linking sediment supply patterns to spatial and temporal variability in channel form and aquatic habitat. To evaluate this model, change over time in three habitat variables is quantified using a 2D hydrodynamic modeling approach. A 45‐year record of topographic data from Carnation Creek, a catchment in coastal British Columbia, is used for the flow modeling. Using the Nays2DH modeling platform, water depths and velocities are simulated in eight channel segments located at different positions relative to locations of historical colluvial input using seven flow levels ranging from 3% to 400% of mean annual discharge (0.02 to 3.31 m 3s −1). Results indicate that habitat availability changes through time as a result of sediment supply‐driven changes to channel morphology and wood loads, but patterns in habitat vary as a function of dominant channel segment morphology. Spatial and temporal variability in morphology also influences the relationship between habitat availability and river discharge, leading to non‐stationary habitat‐discharge rating curves. When habitat areas are predicted by applying these curves to daily flow series spanning annual dry seasons, over 50% of the variance in cumulative seasonal habitat area can be explained by year‐to‐year changes in channel morphology and wood loading, indicating that changing morphology is an important factor for driving temporal habitat variability. This variance is related to the morphological variability of a channel segment, which in turn is associated with the segment position relative to zones of colluvial input. Collectively, these results suggest that variability in habitat is impacted by channel morphology, and can be evaluated partly on the basis of a channel's sediment supply regime. © 2019 John Wiley & Sons, Ltd.

Characterization of morphological units in a small, forested stream using close-range remotely piloted aircraft imagery

“…Upstream, the channel narrows into a canyon (Fig. 1) which contains a predominantly step-pool morphology and gradient above 5 % (Reid et al, 2019). The average bankfull width (w b ) of the lower channel is close to 15 m. The channel is located within the Coastal Western Hemlock biogeoclimatic zone, common along coastal regions of the Pacific Northwest (Hartman et al, 1982).…”

“…The environment is typical of the Pacific Northwest: precipitation rates are high and dominated by rain (between 2900 and 5000 mm yr −1 ), the majority of which falls during the autumn and winter months (Tschaplinski and Pike, 2017). Streamflow ranges from 0.1 to 64 m 3 s −1 in autumn and winter months (Tschaplinski and Pike, 2017) and is often very low (< 0.01 m 3 s −1 ) for extended periods in the summer (Reid et al, 2020). Frequent storms in the winter months lead to multiple floods per year that are capable of mobilizing gravel in the system, with bankfull discharge between 20 and 30 m 3 s −1 (Haschenburger, 2011).…”

“…Traditionally, characterization and classification of channels through field surveys has required the use of a variety of GPS-based tools and linear-survey methods involving automatic levels, theodolites, and total stations (e.g. Bangen et al, 2014;Reid et al, 2019). However, advances in our understanding of connections between geomorphological, hydrological, and ecological processes across the riverscape require a new approach for fluvial characterization that can capture many variables concurrently and be conducted at scales relevant to key processes and their interactions (Beechie et al, 2010).…”

“…To address these questions, a subcanopy RPA survey was conducted along approximately 3.0 km of a channel in Carnation Creek, BC, a small coastal stream located on western Vancouver Island. This site serves as a valuable testing area due to the abundance of complementary data available through annual total station surveys of the channel's study sections and longitudinal profile survey data across the channel's lower 3.0 km (Tschaplinski and Pike, 2017;Reid et al, 2019).…”

CNN for image-based sediment detection applied to a large terrestrial and airborne dataset

“…As noted by several researchers (e.g., Carbonneau et al (2004), Graham et al (2010) and Buscombe (2020)), object-based methods require sophisticated object segmentation algorithms and theoretically cannot be used on grains smaller than one pixel, however, object-based methods can provide grain-scale information on spatial variability which is essential in not only predicting but also understanding the processes of flow resistance (Chen et al, 2020), sediment transport (Yager et al, 2018) and aquatic habitat (Reid et al, 2020). The BASEGRAIN model developed by ETH Zurich is a state-of-art object-based grain sizing software, but it requires extensive parameter tuning (the model contains more than 40 adjustable parameters and seven key parameters) and a significant level of expertise to be applied to sub-optimally captured images.…”