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Water 2017, 9(12), 946; doi:10.3390/w9120946

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

Department of Fisheries and Wildlife, Oregon State University, 104 Nash Hall, Corvallis, OR 97331, USA
U.S. Geological Survey, Forest and Rangeland Ecosystem Science Center, 3200 SW Jefferson Way, Corvallis, OR 97331, USA
Wyoming Game and Fish Department, P.O. Box 850, Pinedale, WY 82941, USA
Author to whom correspondence should be addressed.
Received: 12 October 2017 / Revised: 30 November 2017 / Accepted: 1 December 2017 / Published: 5 December 2017
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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 temperature to diagnose patterns of stream drying. Specifically, we applied HMMs to time series of daily standard deviation (SD) of stream temperature (i.e., dry stream channels typically display highly variable daily temperature records compared to wet stream channels) between April and August (2015–2016). We used information from paired stream and air temperature data loggers as well as co-located stream temperature data loggers with electrical resistors as confirmatory sources of the timing of stream drying. We expanded our approach to an entire stream network to illustrate the utility of the method to detect patterns of flow permanence over a broader spatial extent. We successfully identified and separated signals characteristic of wet and dry stream conditions and their shifts over time. Most of our study sites within the entire stream network exhibited a single state over the entire season (80%), but a portion of them showed one or more shifts among states (17%). We provide recommendations to use this approach based on a series of simple steps. Our findings illustrate a successful method that can be used to rigorously quantify flow permanence regimes in streams using existing records of stream temperature. View Full-Text
Keywords: flow permanence; stream drying; Hidden Markov Models; stream networks flow permanence; stream drying; Hidden Markov Models; stream networks

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Arismendi, I.; Dunham, J.B.; Heck, M.P.; Schultz, L.D.; Hockman-Wert, D. A Statistical Method to Predict Flow Permanence in Dryland Streams from Time Series of Stream Temperature. Water 2017, 9, 946.

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