Basin‐scale methods for predicting salmonid spawning habitat via grain size and riffle spacing, tested in a California coastal drainage

Abstract

Basin‐scale predictive geomorphic models for river characteristics, particularly grain size, can aid in salmonid habitat identification. However, these basin‐scale methods are largely untested with actual habitat usage data. Here, we develop and test an approach for predicting grain size distributions from high resolution LiDAR (Light Detection and Ranging)‐derived topographic data for a 77 km2 watershed along the central California Coast. This approach improves on previous efforts in that it predicts the full grain size distribution and incorporates an empirically calibrated shear stress partitioning factor. The predicted grain size distributions are used to calculate the fraction of the bed area movable by spawning fish. We then compare the ‘movable fraction’ with 7 years of observed spawning data. We find that predicted movable fraction explains the paucity of spawning in the upper reaches of the study drainage, but does not explain variation along the mainstem. In search of another morphologic characteristic that may help explain the variation within the mainstem, we measure riffle density, a proxy for physical habitat complexity. We find that field surveys of riffle density explain 64% of the variation in spawning in these mainstem reaches, suggesting that within reaches of appropriate sized gravel, spawning density is related to riffle density. Because riffle density varies systematically with channel width, predicting riffle spacing is straightforward with LiDAR data. Taken together, these findings demonstrate the efficacy of basin‐scale spawning habitat predictions made using high‐resolution digital elevation models. Copyright © 2016 John Wiley & Sons, Ltd.

Physical Controls on Salmon Redd Site Selection in Restored Reaches of a Regulated, Gravel‐Bed River

“…The majority of pool tail and riffle locations in both reaches contained redds (Figure 9a and 9b), indicating that the areal extent of pool-riffle morphology constituted a limiting resource for redd selection within the restored morphology of the two very different study sites. Results from the restored channels studied here confirm the findings from prior studies of natural channels, which have found higher redd densities near channel features such as alluvial bars, islands, and riffles (Coulombe-Pontbriand & LaPointe, 2004;Geist & Dauble, 1998;Pfeiffer & Finnegan, 2017).…”

“…Our results are in agreement with previous studies that have found that the majority of spawning occurred in areas where F M > 0. 75 (Pfeiffer & Finnegan, 2017;Riebe et al, 2014). Redd construction within coarse substrates of the restored channels studied here was aided by the overall looseness of the bed, as indicated by our force gage measurements and mechanistic predictions of critical Shields stresses τ * c À Á that were on the lower end of the typical τ * c range of 0.03 to 0.06 reported in the literature (Buffington & Montgomery, 1997).…”

Entropy and Intermittency of River Bed Elevation Fluctuations

“…They have been shown to exhibit scale‐dependent geometrical and statistical properties. Investigating their spatial characteristics and evolution is important for river management and interpreting past sedimentary strata, as well as for predictive modeling of sediment transport rates (Best, 2005; Drake et al, 1988; Fernandes et al, 2016; Guala et al, 2014; Jerolmack & Mohrig, 2005; Ma et al, 2014; McElroy & Mohrig, 2009; Nelson et al, 1993; Nikora & Walsh, 2004; Nikora et al, 2002; Nittrouer & Viparelli, 2014; Pfeiffer & Finnegan, 2017; Venditti et al, 2005).…”

A Channel Network Model for Sediment Dynamics Over Watershed Management Time Scales

“…The approaches for modeling shear stress and bedload transport require the characterization of representative grain sizes on the channel bed surface including the median D 50 , 90th percentile D 90 , mean D mean , median gravel D gb , and median sand D S . To assign fixed grain sizes of D 50 , D 90 , D mean , and D gb for each channel Journal of Advances in Modeling Earth Systems segment, we modified approaches of Pfeiffer and Finnegan (2017), Snyder et al (2013), and Barry et al (2004). We assumed a constant bankfull Shields stress τ * bf for each segment j and solve for grain size at each segment based on its predicted τ * bf .…”