EARLY HEARING DETECTION AND INTERVENTION VIRTUAL CONFERENCE
MARCH 2-5, 2021
(Virtually the same conference, without elevators, airplane tickets, or hotel room keys)
5/25/2021 | 8:30 AM - 10:30 AM | CLASSIFYING FRESHWATER SALINIZATION REGIMES IN RIVERS AND STREAMS DRAINING THE WESTERN UNITED STATES | Virtual Platform
CLASSIFYING FRESHWATER SALINIZATION REGIMES IN RIVERS AND STREAMS DRAINING THE WESTERN UNITED STATES
Freshwater salinization (FS) is increasing across the globe due to non-point source loading of ions originating from anthropogenic activities such as agricultural and urban land-use. Understanding of decadal trends in FS is improving, but the spatial distributions of the timing, magnitude, and duration of characteristic annual patterns of river salinity and the relationship between annual regime types and catchment attributes remains unclear. We classified annual salinity regimes in rivers draining the western U.S. by clustering daily specific conductance (SC) time series from 244 U.S. Geological Survey (USGS) gages distributed throughout 17 states and two monitoring sites in the Rio Grande, which we flow-normalized (SC/Q) using USGS discharge (Q) data. We found two regimes: cluster 1 (73% of sites) characterized by an early-fall peak in SC/Q, and cluster 2 (28% of sites) characterized by a late-winter or early-spring peak. We used a random forest classifier and found that soil salinity and proportional area of open water were the most important catchment attributes for predicting cluster membership. Our results will improve our ability to predict the timing of excess salinity and inform mitigation of FS impacts on lotic ecosystems.
- Machine learning
- Flow
- Conductivity
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Presenters/Authors
Lauren Bolotin
(), University of Nevada Reno, bolotinljb@gmail.com;
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Joanna Blaszczak
(), Global Water Center and Department of Natural Resources and Environmental Science, University of Nevada, Reno, jblaszczak@unr.edu;
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Betsy Summers
(), University of New Mexico, shaferbetsy@gmail.com;
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