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A Statistical Method to Predict Flow Permanence in Dryland Streams from Time Series of Stream Temperature

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Ivan Arismendi, Jason Dunham, Michael Heck, Luke Schultz, David Hockman-Wert, Arismendi, Ivan, Dunham, Jason B., Heck, Michael P., Schultz, Luke D., and Hockman-Wert, David, A Statistical Method to Predict Flow Permanence in Dryland Streams from Time Series of Stream Temperature: Water, v. 9, iss. 12.

Summary

Abstract (from MDPI): Intermittent and ephemeral streams represent more than half of the length of the global river network. Dryland freshwater ecosystems are especially vulnerable to changes in human-related water uses as well as shifts in terrestrial climates. Yet, the description and quantification of patterns of flow permanence in these systems is challenging mostly due to difficulties in instrumentation. Here, we took advantage of existing stream temperature datasets in dryland streams in the northwest Great Basin desert, USA, to extract critical information on climate-sensitive patterns of flow permanence. We used a signal detection technique, Hidden Markov Models (HMMs), to extract information from daily time series of stream [...]

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  • National CASC
  • National and Regional Climate Adaptation Science Centers

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Categories
Water, Coasts and Ice
Drought, Fire and Extreme Weather
Organization
NCCWSC Science Themes
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journalWater
parts
typevolume
value9
typeissue
value12
typestartPage
value946
typedoi
value10.3390/w9120946

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