ArticleEnvironmental science & technology2019
Modeling Spatial and Temporal Variation in Natural Background Specific Conductivity.
Article in Environmental science & technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A model-based assessment of anthropogenic disturbance on lotic macroinvertebrate assemblages.Ecosphere (Washington, D.C) · 2025Article
- Regional high-frequency monitoring revealed chloride concentrations in exceedance of ecological benchmarks in urban streams across the Delaware River Basin, USA.Environmental monitoring and assessment · 2025Article
- Prioritizing US Geological Survey science on salinization and salinity in candidate and selected priority river basins.Environmental monitoring and assessment · 2024Article
- Predictive Modeling Reveals Elevated Conductivity Relative to Background Levels in Freshwater Tributaries within the Chesapeake Bay Watershed, USA.ACS ES&T water · 2024Article
- Predictive Understanding of Stream Salinization in a Developed Watershed Using Machine Learning.Environmental science & technology · 2024Article
- Using models of local environmental conditions for biological assessment.Freshwater science (Print) · 2024Article
- Modeling lake conductivity in the contiguous United States using spatial indexing for big spatial data.Spatial statistics · 2024Article
- Development of national stressor-specific genus sensitivity values.The Science of the total environment · 2023Article
- Common irrigation drivers of freshwater salinisation in river basins worldwide.Nature communications · 2021Article
- A flow-chart for developing water quality criteria from two field-based methods.The Science of the total environment · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Understanding how background levels of dissolved minerals vary in streams temporally and spatially is needed to assess salinization of fresh water, establish reasonable thresholds and restoration goals, and determine vulnerability to extreme climate events like drought. We developed a random forest model that predicts natural background specific conductivity (SC), a measure of total dissolved ions, for all stream segments in the contiguous United States at monthly time steps between the years 2001 to 2015. Models were trained using 11 796 observations made at 1785 minimally impaired stream segments and validated with observations from an additional 92 segments. Static predictors of SC included geology, soils, and vegetation parameters. Temporal predictors were related to climate and enabled the model to make predictions for different dates. The model explained 95% of the variation in SC among validation observations (mean absolute error = 29 μS/cm, Nash-Sutcliffe efficiency = 0.85). The model performed well across the period of interest but exhibited bias in Coastal Plain and Xeric regions (26 and 30%, respectively). National model predictions showed large spatial variation with the greatest SC predicted to occur in the desert southwest and plains. Model predictions also reflected changes at individual streams during drought.
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