Best-fit Sediment Models
Our best-fit models are statistically significant predictors of SSC in tributaries to Lake Peters, although there is considerable unexplained variability (Table 3). Mass wasting independent of discharge (Gao, 2008; Hammer & Smith, 1983; Hasholt et al., 2005; Walker & Hudson, 2003), and sediment pulses associated with glacier motion (Hasholt et al., 2005; Willis et al., 1996), may cause transient flushes of sediment not accounted for by our models. Compared with Carnivore Creek, the consistency and accuracy of SSC modeling is lessor in Chamberlin Creek, which has a smaller sub-catchment size. This is relatable to the inherent flashiness of smaller catchments (Horowitz, 2003), making complex sediment transfer processes difficult to quantify, even with continuous and high-resolution model predictors. NTU-based models outperformed Q-based models as a predictor of SSC, which is not surprising given SSC can vary by two orders of magnitude for any one discharge (Morehead et al., 2003), whereas turbidity is a more direct surrogate for SSC. In contrast to our models (Table 3), earlier multiple-regression sediment models developed for arctic rivers have not incorporated NTU, but have favored alternative discharge variables (ΣQ, ΔQ, QE, and/or Q2), with positive and/or negative coefficients depending on the catchment, season, or sub-season (Hodgkins, 1999; Hodson and Ferguson, 1999; Irvine-Fynn et al., 2005; Schiefer et al., 2017). Further, these earlier models have been geographically limited to catchments in Svalbard. In all cases at Lake Peters, inclusion of additional meteorological or temporal predictor variables (uncorrelated with NTU or Q) improved performance of models predicting SSCs (Table 3), and supported our understanding of sediment transfer processes over two years of open-channel flow. The best-fit model predictors could not be used standalone to interpret physical processes, because exclusion of correlated predictors to avoid overfitting masked some meteorology–hydrology interactions, and numerous predictor combinations provided statistically significant model outputs. Therefore, further analysis of the hydrometeorological data, as well as qualitative geomorphologic evaluations, informed our understanding of sediment transfer processes at Lake Peters.