4
Discussion
4.1 Modeling streamwater chemistry
in burned
watersheds
Multiple lines of evidence indicated that stream
NO3- concentrations had greater
spatial variability and weaker spatial structuring relative to
Na+. First, semivariance was greater for stream
NO3- than Na+ across
all flow-connected distances (Figure 3) which suggests higher
variability in stream NO3-concentrations across all measured scales (Isaak et al., 2014).
Secondly, the nugget effect was orders of magnitude greater for stream
NO3- than Na+ (0.385
and 0.001 respectively) which indicates unmeasured fine-scale
variability in stream NO3-concentrations (Cooper et al., 1997). Finally, Na+semivariance increased with lag distance and stabilized around 3,700 m
(Figure 3). This strong positive autocorrelation indicates that
downstream hydrologic transport was the primary driver of spatially
distributed Na+ concentrations. In contrast, the
empirical semivariogram for NO3-exhibited irregular trends in semivariance that did not stabilize across
the measured range in spatial scales (Figure 3).
SSN model improvements varied with the solute of concern and network
position. For Na+, the SSN model reduced the AIC by
61%, RMSPE by 20%, and unexplained variance by 96% compared to the
MLR model (Table 4). In contrast, the
NO3- SSN model only reduced the AIC by
<1%, the RMSPE by 7%, and the unexplained variance by 54%
(Table 4). SSN model improvements tend to be smaller where spatial
autocorrelation is lower (Isaak et al., 2014) such as with
NO3- at our sites. Additionally, SSN
models improved predictions more in downstream positions whereas MLR
prediction error was relatively consistent across network positions
(Figure 4C-D). Moving downstream, SSN models are informed by an
increasing number of upstream data points. Conversely, SSN predictions
in headwater locations rely more on watershed attributes than upstream
data, much like MLR models.
4.2 Post-fire vegetation is a
dominant driver of stream NO3-patterns
Large high severity fire has the potential to shift ecosystems from
forest to grass and shrubland which can have implications for watershed
N cycling. Even decades after the Hayman and nearby fires, 75% of high
severity plots had no conifer regeneration and it is possible that
forest density will never return to pre-fire levels in these areas
(Chambers et al., 2016). Beyond our field sites, there is broad evidence
of declining post-fire tree regeneration due to increasing climate
aridity and fire activity which can shift previously forested systems
into alternative stable states dominated by grassland and weedy,
herbaceous vegetation types (Coop et al., 2020; Stevens-Rumann et al.,
2018; Tepley et al., 2017; Walker et al., 2018). Forest cover is often a
primary mechanism for terrestrial N retention (Dunnette et al., 2014;
Vitousek et al., 1979) and changes from forest to grass and shrub cover
can impact ecosystem N retention (Lovett et al., 2002). For example,
conifers will more strongly regulate N cycling than grasses and forbs
given their underlying nutrient use efficiencies (Chapman et al., 2006).
Therefore, post-fire watersheds with little tree regeneration will
likely be leakier with respect to N cycling.
Spectral vegetation indices were the strongest predictors of stream
NO3- in this and other studies. For
example, reduced post-fire plant cover, measured as NDVI, explained the
persistence of elevated post-fire stream N (Rust et al., 2019). In this
study though, the strongest predictor of stream
NO3- concentration was mean NDMI
(Table 3), a vegetation index that considers both canopy cover and the
water stress of that vegetation. NDMI is more sensitive to burn
severity, forest type, and forest loss and recovery than NDVI which is
broadly sensitive to the amount of photosynthetically active vegetation
(Morresi et al., 2019). The strong inverse relationship between NDMI and
stream NO3- demonstrates that
vegetation cover was a primary control on watershed N retention across
spatial scales and the loss of forest cover lead to elevated stream
NO3-. This is consistent with earlier
work demonstrating that stream NO3-concentrations were inversely related to riparian vegetation exposure
(Rhoades et al., 2019).
Rapid in-stream uptake and processing contribute to variability in
stream NO3- concentrations (Bernhardt
et al., 2003). Nitrate uptake lengths in nearby Wyoming streams ranged
from hundreds to thousands of meters (Hall et al., 2009), so uptake is
likely to influence NO3- patterns
across the range of scales in our study (<9,000 m). However,
headwater streams with elevated ambient inorganic N concentrations have
a limited ability to moderate downstream transport of inorganic N
(Covino et al., 2021b) because nutrient delivery to streams is often
orders of magnitude greater than in-stream production or removal
(Brookshire et al., 2009). Our previous work at the Hayman Fire
demonstrated that in-stream biotic N demand increased after the fire,
but N supply from burned uplands exceeded the increase in stream N
demand (Rhea et al., 2021). While in-stream uptake likely contributed to
spatial variability in stream NO3-,
our work demonstrates strong post-fire vegetation controls on the
spatial patterns of stream NO3-concentrations.
4.3 Burned headwaters are
susceptible to elevated stream NO3-
Patterns of vegetation cover interact with watershed structure to drive
spatial distributions of stream NO3-concentrations. Terrestrial inputs of water and dissolved solutes
comprise a large portion of streamwater composition in headwater
positions, making these areas particularly sensitive to disturbance in
the surrounding uplands (Gomi et al., 2002; Likens & Bormann, 1974;
Lowe & Likens, 2005). Thus, the vegetation cover of large convergent
hillslopes should have stronger proportional influence on stream
NO3- concentration in headwater
positions relative to locations lower in the network. We found that
convergent hillslopes in the headwaters of Brush Creek were associated
with low NDMI (Figure 5E) and aligned with locations of high stream
NO3- (Figure 5E). Proportional inflows
declined downstream and were associated with higher NDMI. Stream
NO3- also declined
downstream in Brush Creek, likely due to a combination of reduced
proportional influence of hillslope inputs, streamflow dilution, and
in-stream N uptake. In the unburned headwaters of Pine Creek, convergent
hillslopes were associated with high NDMI (Figure 5F) and likely high
terrestrial N demand. Stream NO3‑concentrations remained low throughout the headwaters with only slight
downstream increases where hillslopes were sparsely vegetated (Figure
5F).
This investigation demonstrates that convergent hillslopes in headwater
positions are particularly sensitive to wildfire-induced vegetation
mortality and can impact both local and downstream water quality.
Headwater attributes have been shown to predict downstream water
chemistry (i.e., NO3-,
PO43-, Ca2+, and
Sr2+) at distances > 500 km (French et
al., 2020). The sampled stream networks were only 5,520 - 8,289 m, so
headwater attributes could feasibly influence downstream chemistry
throughout the entire stream networks. Indeed, the watershed with burned
headwaters (i.e., Brush), sustained higher stream
NO3- concentrations throughout its
stream network compared to the watershed with unburned headwaters (i.e.,
Pine, Figure 5E-F). These findings may help prioritize post-fire
watershed rehabilitation efforts aimed at increasing plant cover and
nutrient demand to reduce stream NO3-concentrations. More specifically, our findings highlight the potential
value for post-fire regeneration in convergent headwater locations to
enhance N retention and reduce downstream
NO3- export.