Freshwater salinization of rivers is occurring across the globe because of non-point source loading of salts from anthropogenic activities such as agriculture, urbanization, and resource extraction that accelerate weathering and release salts. Multi-decadal trends in river salinity are well characterized, yet our understanding of annual regimes of salinity in rivers draining diverse western U.S. landscapes and their associated catchment attributes is limited. We classified annual salinity regimes in 242 stream locations through dynamic time warping and fuzzy c-medoids clustering of salinity time series. We found two dominant regimes in salinity characterized by an annual summer-fall peak or spring decline. Using random forest regression, we found that precipitation amount, stream slope, and soil salinity were the most important predictors of salinity regime classification. Advancing our understanding of salinity regimes in rivers will improve our ability to predict and mitigate the effects of salinization in freshwater ecosystems through management interventions. All code and data used in the analysis of this project are included in this repository.
