Note: TWI: topographic wetness index, Bare is bare ground cover; NDMI: normalized differenced moisture index, NDVI: normalized differenced vegetation index, EVI: enhanced vegetation index, dNBR: differenced normalized burn ratio, and MTBS: monitoring trends in burn severity database.
Topographic metrics included watershed area, mean slope, mean elevation, riparian extent, and mean topographic wetness index (TWI) (Table 3). Slope, elevation, and TWI were derived from the 10-m DEM using Whitebox tools (Lindsay, 2020; Wu, 2021) and summarized as watershed means. We used a physical definition of the riparian corridor that included pixels <2 m above the stream surface elevation (sensu Jencso et al., 2010) and calculated riparian extent as the total riparian corridor area divided by UAA of each sampling point. This approach differs from an earlier estimate of the extent of riparian vegetation in these watersheds (Rhoades et al., 2019).
We characterized vegetation condition using normalized differenced vegetation index (NDVI), normalized differenced moisture index (NDMI), and enhanced vegetation index (EVI). We obtained mean June 2018 vegetation indices from Landsat using Climate Engine (Huntington et al., 2017) to match the vegetation characterization with the timing of our stream sampling. We also included 2018 fractional land cover estimates derived from satellite imagery that was extensively calibrated across the Western US and estimated the proportion of each Landsat pixel covered by trees, shrubs, and bare ground (Allred et al., 2021).
Mean differenced normalized burn ratio (dNBR, a measure of burn severity) and burn extent were calculated for the area contributing to each sampling point. These fire metrics represent immediate post-fire impacts by differencing one pre-fire (8/24/2001) and one post-fire (8/14/2003) Landsat image. dNBR was then classified into categorical burn severity as follows: -150-140 unburned; 140-211 low severity; 211-350 moderate severity, 350-953 high severity (Eidenshink et al., 2009). Low severity fire tends to leave tree canopies largely unaltered whereas high severity fire typically causes complete consumption of surface organic matter and canopy foliage (Parsons et al., 2010). Wildfire severity varies spatially across topographic, vegetation (i.e., fuel composition, arrangement, condition), and weather gradients (Taylor et al., 2021) which creates mosaics of post-fire vegetation structure and composition that vary at scales finer than mapped severity patches (Lentile et al., 2007). To characterize the spatial burn patterning of each watershed, we calculated burn extent, patch size, patch radius, and patch density by severity (Table 2). Burn extent reflects the proportion of watershed area that was burned by each severity class. All patch metrics were calculated with the landscape metrics package (Hesselbarth et al., 2019) in R Studio which defines contiguous cells belonging to the same burn severity class. For each watershed, we determined patch area and calculated patch radius as the mean distance from each cell in a patch to its centroid, and patch density as the number of patches divided by watershed UAA.

2.4 Statistical modeling

We used statistical models to evaluate the degree to which topographic, vegetation, and fire variables and flow-connected distance control post-fire stream water chemistry – specifically, concentrations of Na+ and NO3-. Concentration data were log-transformed to improve normality and a correlation analysis removed redundant predictor variables with a correlation >0.90 (Figure 2, Table 2). To identify the top-performing Na+ and NO3- models, we went through a two-step model selection process (sensu McManus et al., 2020; Rodríguez-González et al., 2019). First, we identified which landscape characteristics best predicted stream Na+ and NO3- using linear mixed model selection (Supplemental Table 1). The Na+ and NO3- models with the lowest maximum likelihood estimate of Akaike’s Information Criteria (AIC) then progressed to the second phase of model selection where we compared spatial autocorrelation approaches. We initially ran multiple linear regression (MLR) models which use landscape characteristics to predict observed water chemistry at each sampling location. The predictor variables are spatially explicit given that they characterize the area contributing to a specific sampling point, but MLR models assume independence between stream water samples. We then compared MLR models to spatial stream network (SSN) models that jointly consider landscape and stream network characteristics. This approach captures spatial effects beyond those directly attributable to predictor variables by accounting for flow-connection (Isaak et al., 2014). MLR and SSN model performance was compared through iterative leave-one-out cross-validation. Observations at sampling points were removed one at a time and the model was used to predict each of the removed values along with the prediction standard error (Ver Hoef & Peterson, 2020). The model with the lowest AIC and root mean square prediction error (RMSPE) was selected for subsequent analyses.