5
Conclusions
This study utilized spatially distributed stream solute sampling to
identify the controls on stream Na+ and
NO3- concentrations across a gradient
of burn patterns. Statistical modeling was used to partition the
variance in stream Na+ and
NO3- among landscape (i.e.,
topographic, vegetation, and fire predictors) and stream network (i.e.,
flow-connected distance) characteristics. Topographic, vegetation, and
fire variables were poor predictors of stream Na+whereas mean NDMI was the strongest predictor of stream
NO3-. Strong positive spatial
autocorrelation indicated that downstream hydrologic transport was the
primary driver of spatially distributed Na+concentrations. Conversely, stream
NO3- exhibited high spatial
variability and weak spatial structure across all spatial scales. These
results suggest that complex wildfire patterns that create a mosaic of
unburned forest interspersed with patches of shrubs and grasses can
result in high variability in stream
NO3- concentrations. We also found
that sparse forest cover in severely burned convergent hillslopes in
headwater positions had a disproportionate impact on stream
NO3- concentrations, suggesting that
targeted reforestation in these locations may help limit stream
NO3- concentrations and downstream
export.
6 Acknowledgments and Data
We are grateful for financial support from the US Forest Service
National Fire Plan (2016-2019) and the Joint Fire Sciences Program
(JFSP# 14-1-06-11). AR was supported by NASA Headquarters under the
NASA Earth and Space Science Fellowship Program. Sincere thanks to Tim
Fegel of the Rocky Mountain Research Station for his critical
contributions to field and laboratory work. The authors declare no
conflicts of interest. The data used in this paper are publicly
available through CUASHI HydroShare: Rhea, A. (2022). Use of
geostatistical models to evaluate landscape and stream network controls
on post-fire stream nitrate concentrations, HydroShare ,
http://www.hydroshare.org/resource/e35a308d4672419c9f75f6897c823c92.