Abstract
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.
Highlights
Perhaps one of the most fundamental issues concerning flow permanence in stream channels is the lack of understanding about expected spatial and temporal patterns across landscapes, riverscapes, or stream networks [1,2]
We initially considered several descriptors of daily stream temperature to be included as time series to diagnose stream drying using Hidden Markov Models (HMMs) (Figure 2)
The timing and type of state identified by each HMM closely tracked visual convergence of daily standard deviation (SD) in stream channels and air temperatures as channels dried
Summary
Perhaps one of the most fundamental issues concerning flow permanence in stream channels is the lack of understanding about expected spatial and temporal patterns across landscapes, riverscapes, or stream networks [1,2]. Many studies, ranging from work conducted in the 1950s [3] to more contemporary assessments [4,5], consistently point to inaccuracies in characterization of flow permanence on existing maps. Efforts to improve quantification of flow permanence in the field have involved a host of techniques ranging from direct observation to various forms of instrumentation [6,7]. Indirect quantification of flow permanence via time series of temperature recorded. Water 2017, 9, 946 in stream channels is promising. These instrumental records are already available in many regions, often comprising datasets consisting of thousands of individually monitored sites [8]. Stream temperature records that indicate a dry stream channel are often edited or deleted, without considering the value of such information in diagnosing drying
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