Sediment Models
SSCs measured in Carnivore and Chamberlin Creeks were modeled using a
combination of hydrologic, meteorological, and temporal variables. The
modeling periods are the 2015 and 2016 open-channel seasons, which
encompass the vast majority of annual sediment transfer to Lake Peters.
To develop multiple-regression models of SSC for Carnivore and
Chamberlin Creeks, a similar approach to Hodgkins (1999) and Schiefer et
al. (2017) was used. We assessed 60 potential predictors: a range of
hydrological, meteorological, and temporal explanatory variables, all
linearly interpolated to match times of SSC sampling (Table 1). The
frequency of SSC sampling was sufficiently discrete that when we tested
for serial autocorrelation, a negative result was returned, thus no
adjustment was necessary.
Correlations among predictor variables were calculated in R software
(Thurston, 2017). Correlated variables (p < 0.05), which could
introduce spurious relations if input as covariates, were grouped to
ensure that they would not be selected in the same model. A for-loop was
constructed to cycle through the correlated groups, applying the
‘glmulti’ function for exhaustive candidate testing (Calcagno &
Mazancourt, 2010) for each sub-catchment separately (Thurston, 2017).
Akaike’s (1977) information criterion (AIC) was used to assess the
relative goodness of fit for each candidate model, while avoiding
overfitting (Burnham & Anderson, 2002), and statistics for the best
models with similarly low AICs were compared.