Presentation
How Well Can We Predict Salmonid Spawning Habitat with LiDAR
AGU Fall Meeting Abstracts
(2013)
Abstract
Suitable salmonid spawning habitat is, to a great extent, determined by physical, landscape driven characteristics such as channel morphology and grain size. Identifying reaches with high-quality spawning habitat is essential to restoration efforts in areas where salmonid species are endangered or threatened. While both predictions of suitable habitat and observations of utilized habitat are common in the literature, they are rarely combined. Here we exploit a unique combination of high-resolution LiDAR data and seven years of 387 individually surveyed Coho and Steelhead redds in Scott Creek, a 77 km2 un-glaciated coastal California drainage in the Santa Cruz Mountains, to both make and test predictions of spawning habitat. Using a threshold channel assumption, we predict grain size throughout Scott Creek via a shear stress model that incorporates channel width, instead of height, using Manning's equation (Snyder et al., 2013). Slope and drainage area are computed from a LiDAR-derived DEM, and channel width is calculated via hydraulic modeling. Our results for median grain size predictions closely match median grain sizes (D50) measured in the field, with the majority of sites having predicted D50's within a factor of two of the observed values, especially for reaches with D50 > 0.02m. This success suggests that the threshold model used to predict grain size is appropriate for un-glaciated alluvial channel systems. However, it appears that grain size alone is not a strong predictor of salmon spawning. Reaches with a high (>0.1m) average predicted D50 do have lower redd densities, as expected based on spawning gravel sizes in the literature. However, reaches with lower (<0.1m) predicted D50 have a wide range of redd densities, suggesting that reach-average grain size alone cannot explain spawning site selection in the finer-grained reaches of Scott Creek. We turn to analysis of bedform morphology in order to explain the variation in redd density in the low-slope, finer-grained reaches of Lower Scott Creek. Because spawning is strongly correlated with riffle locations, we use a LiDAR-derived longitudinal profile to predict where riffle habitat is located within the watershed. To accomplish this, we use previous studies that constrain pool-riffle habitat to slopes <1.5%, then use wavelet analysis of the longitudinal profile within these pool-riffle reaches to investigate the spacing of drops in water surface slope, with the goal of identifying reaches with high riffle density. Our slope-based predictions of pool-riffle morphology closely match the extent of pool-riffle reaches observed in the field. Average redd density in pool-riffle reaches is more than double the average redd density in reaches of other channel morphologies. Initial wavelet analysis suggests that riffle spacing may be longer in the lower reaches of Scott Creek and shorter in the high-redd density upper reaches, a finding that agrees with the hypothesis that spawning habitat is limited by riffle density. Our results suggest that high resolution topographic data can be successfully used to identify reaches of utilized spawning habitat based on grain size predictions and wavelet analysis of bedform spacing.
Keywords
- Hydrology,
- Geomorphology,
- Salmonid spawning habitat
Disciplines
Publication Date
December, 2013
Location
San Francisco, CA
Citation Information
Pfeiffer, A.M., N. Finnegan, and S. Hayes. 2013. How well can we predict salmonid spawning habitat
with LiDAR? American Geophysical Union. San Francisco, December.