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Modeling Spatial and Temporal Variation in Natural Background Specific Conductivity.

John R Olson1, Susan M Cormier2

  • 1California State University Monterey Bay , School of Natural Sciences , 100 Campus Center , Seaside , California 93955 , United States.

Environmental Science & Technology
|March 13, 2019
PubMed
Summary
This summary is machine-generated.

A new random forest model predicts natural background specific conductivity (SC) in U.S. streams. This tool helps assess freshwater salinization and drought vulnerability across the contiguous United States.

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Area of Science:

  • Environmental Science
  • Hydrology
  • Water Quality Assessment

Background:

  • Understanding temporal and spatial variations in stream dissolved mineral levels is crucial for assessing freshwater salinization.
  • Assessing background mineral levels is essential for setting restoration goals and identifying vulnerabilities to climate events like drought.

Purpose of the Study:

  • To develop a predictive model for natural background specific conductivity (SC) in all stream segments across the contiguous United States.
  • To provide monthly predictions of SC from 2001 to 2015, aiding in water resource management and environmental monitoring.

Main Methods:

  • A random forest model was developed using 11,796 observations from minimally impaired streams.
  • Static predictors (geology, soils, vegetation) and temporal predictors (climate) were used to train and validate the model.
  • Model validation involved an additional 92 stream segments, achieving high accuracy.

Main Results:

  • The model explained 95% of the variation in SC among validation observations, with a mean absolute error of 29 microS/cm.
  • High predictive accuracy (Nash-Sutcliffe efficiency = 0.85) was achieved across the study period, though bias was noted in specific regions.
  • National predictions revealed significant spatial variation in SC, with higher levels anticipated in the desert southwest and plains, and reflected drought impacts.

Conclusions:

  • The developed random forest model accurately predicts background specific conductivity in U.S. streams, offering a valuable tool for water quality assessment.
  • The model's ability to capture temporal and spatial variations aids in understanding salinization dynamics and climate change impacts on freshwater resources.
  • Findings highlight the importance of considering geological, soil, vegetation, and climate factors in predicting stream water quality.