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.