3 Results

3.1 Stream Na+ and NO3-concentrations

Observed stream Na+ concentrations ranged from 3.9-13.1 mg/L (Supplemental Figure 1), with an average concentration of 7.3 mg/L which is similar to the pre-fire average of 6.1 mg/L reported in these granitic basins (Rhoades et al., 2011). Kelsey had the highest and Brush had the lowest mean stream Na+ concentration whereas Brush had the highest and West Turkey had the lowest coefficient of variation (Table 1). Observed stream NO3- concentrations varied by three orders of magnitude (0.005 – 6.2 mg/L) and average stream NO3- concentration was 0.91 mg/L which is five times greater than pre-fire concentrations (0.18 mg/L) (Rhoades et al., 2019). Brush watershed had the highest (3.06 mg/L) and Gunbarrel the lowest (0.16 mg/L) mean NO3-concentration whereas Fourmile had the greatest and West Turkey the lowest coefficient of variation (Table 1). The coefficient of variation was consistently higher for stream NO3- (Table 1) indicating greater within-watershed variability in stream NO3- compared to Na+.

3.2 Landscape controls on Na+ and NO3-

Topographic, vegetation, and fire predictor variables were weakly correlated (≤0.33) with log[Na+] (Table 3). Linear mixed model selection identified watershed area, slope, riparian extent, TWI, and tree and shrub cover as the best predictors of log[Na+] (Supplemental Table 1). Stream Na+ was related positively to watershed area, slope, riparian extent, and tree cover, and negatively to TWI, and shrub cover (Figure 2). All watershed predictors were significant in the Na+ MLR model and together explained 54.4% of the variance in log[Na+] (Table 4). Predictor variables explained 45% of the variance in log[Na+] in the Na+ SSN model and all predictors except watershed area were significant (Table 4).
Table 4: Summary of spatial stream network (SSN) and multiple linear regression (MLR) models that predict log-transformed stream Na+ and NO3-concentrations. Parameter estimates represent the regression coefficient, which is the change in the response variable based on a 1-unit change in the predictor variable while holding all other variables constant. Statistical significance of predictor variables is denoted with symbols *=0.05, **=0.01, ***=0.001. Variance decomposition assigns variance in Na+ or NO3- to watershed predictor variables, flow-connected autocorrelation, and unexplained variance. MLR models do not account for flow-connected autocovariance. Model performance metrics come from iterative leave-on-out cross-validation.