1
Introduction
Wildfires are a natural part of many forested ecosystems, but the
frequency and severity of wildfires has been increasing across the
Western US (Abatzoglou et al., 2017; Westerling, 2016). Elevated
wildfire activity can threaten the function of critical forested
watersheds that supply clean water to much of the Western US (Brown et
al., 2008). Nitrogen (N) typically limits plant growth so N export often
indicates ecosystem disturbance and shifts in nutrient supply and demand
(Chapin et al., 2011). Short-term (<5 years) increases in
stream nitrate (NO3-) have been
documented following wildfires across the Western US (Rust et al., 2018;
Smith et al., 2011) due to elevated soil N mineralization and leaching
(Smithwick et al., 2009; Turner et al., 2007; Wan et al., 2001). In some
cases, stream NO3- can remain elevated
for decades and has been shown to decrease with post-fire vegetation
cover (Rhoades et al., 2019; Rust et al., 2019) and increase with burn
extent (Rhoades et al., 2019). These results suggest that a lack of
vegetation recovery is likely a dominant driver of persistent post-fire
NO3- export, but this relationship
remains poorly understood.
The interaction of vegetation cover, watershed structure, and stream
network geometry regulates watershed solute export (Abbott et al., 2021;
Covino et al., 2021; Creed & Beall, 2009; Likens & Bormann, 1974;
Lovett et al., 2002; Shogren et al., 2021; Zarnetske et al., 2018).
Watershed structure is the spatial arrangement of divergent and
convergent hillslopes across the landscape (Baiamonte & Singh, 2016;
Jencso et al., 2010). Divergent hillslopes are convex and contribute
little flow to the stream, whereas convergent hillslopes concentrate
hydrologic flowpaths and contribute large inputs to channel networks
(Detty & McGuire, 2010). In headwater positions, water and solutes are
primarily derived from shallow groundwater contributions from adjacent
hillslopes (Covino et al., 2021; Gomi et al., 2002; Likens & Bormann,
1974) whereas upstream sources increasingly dominate water composition
in lower network positions (Vannote et al., 1980). Therefore, headwaters
are particularly sensitive to disturbance in the surrounding uplands
(Lowe & Likens, 2005) and contributions to the stream in these
locations have the potential to exert strong control on downstream
solute concentrations (Abbott et al., 2018; Alexander et al., 2007;
Wohl, 2017).
To better understand the spatial patterns in post-fire water chemistry,
we consider both conservative and reactive solutes. Conservative
solutes, such as sodium (Na+), have low biological
demand (Dingman, 2015; Stream Solute Workshop, 1990) and thus are
primarily driven by physical transport processes (Webster & Valett,
2006) and watershed geophysical properties (Brennan et al., 2016; French
et al., 2020; McGuire et al., 2014). In contrast, biologically active
solutes such as NO3- are controlled by
interactions between hydrologic transport and biological uptake
(Bernhardt et al., 2003, 2005). In particular, forest cover can be a
primary control on NO3- export at the
watershed scale (Bormann & Likens, 1967; Likens et al., 1970).
Statistical models can be used to partition the spatial variance in
stream Na+ and NO3-among landscape (i.e., topographic, vegetation, and fire predictors) and
stream network (i.e., flow-connected distance) characteristics. Multiple
linear regression (MLR) modeling can be used to determine the relative
influence of specific landscape characteristics on spatially distributed
solute concentrations (Cho & Lee, 2018; McManus et al., 2020), but this
approach assumes independence of sampling locations. Geostatistical
modeling approaches, such as spatial stream network (SSN) models, are
better suited to differentiate landscape from stream network attributes
since they account for spatial autocorrelation of flow-connected samples
and the dendritic and unidirectional nature of stream networks (Ver Hoef
et al., 2014; Isaak et al., 2014; Peterson & Ver Hoef, 2010). We paired
spatially distributed water chemistry sampling with terrain analysis and
vegetation and fire mapping to address the following objectives: 1)
examine the degree to which topographic, vegetation, and fire variables
predict stream Na+ and
NO3- across spatial scales and 2)
evaluate the performance of MLR and SSN models in predicting stream
solute concentrations. To our knowledge, this study is the first to use
geostatistics to investigate the drivers of elevated post-fire stream
NO3‑.