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

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.

Temperature Loggers Capture Intraregional Variation of Inundation Timing for Intermittent Ponds

“…Although two-state HMMs have been applied in past work (Arismendi et al, 2017), we found that two-state HMMs appeared to over-or underestimate inundation duration for several ponds or predict additional wet states when we were confident that the ponds were dry (Figure S3 in Supporting Information S1). Using three-state HMMs and subsequently combining multiple wet or dry states provided more accurate and consistent state predictions between pond only and paired pond-control data sets (Tables S2 and S3 in Supporting Information S1).…”

“…Daily temperature variance is typically lower in water than in air, and comparison of daily temperature variance provides a reliable proxy for inundation state (Sowder & Steel, 2012). A rapid drop in daily temperature variance can reliably measure the precise timing of an inundation event (Anderson et al, 2015;Arismendi et al, 2017). For example, Anderson et al (2015) tested the ability of temperature sensors to accurately predict inundation states both in natural wetlands and in controlled mesocosms that varied in size and depth.…”

Variability of stream extents controlled by flow regime and network hydraulic scaling

“…Headwater stream discharge and network extent-and their variability in time-impact aquatic habitat, carbon dioxide efflux, stream temperature, water transit times, and legal frameworks that define protected waters (Acuña et al, 2005;Allen & Pavelsky, 2018;Arismendi et al, 2017;van Meerveld et al, 2019;Acuña et al, 2014 Early studies of wetted channel extent dynamics showed that higher flows at the catchment outlet were associated with higher total wetted channel lengths (L, sometimes expressed as a drainage density, defined as L normalized by catchment area, and including both continuous and disconnected reaches), as revealed by plots of stream discharge at the catchment outlet (Q, normalized by catchment area) as a function L (e.g., D. G. Day, 1978;Gregory & Walling, 1968;Roberts & Archibold, 1978;Roberts & Klingeman, 1972). Biswal and Marani (2010) then formalized the notion that L controls Q in an investigation on the hydrograph recession.…”

“…Headwater stream discharge and network extent—and their variability in time—impact aquatic habitat, carbon dioxide efflux, stream temperature, water transit times, and legal frameworks that define protected waters (Acuña et al, 2005; Allen & Pavelsky, 2018; Arismendi et al, 2017; van Meerveld et al, 2019; Acuña et al, 2014; A. S. Ward et al, 2018, e.g., the U.S. Clean Water Act and Navigable Waters Protection Rule ). While underlying physical drivers of stream discharge have been extensively studied, controls on time variation in wetted channel extent remain poorly understood.…”

Variability of headwater stream network extents controlled by flow regime and network hydraulic scaling

“…Headwater stream discharge and network extent-and their variability in time-impact aquatic ecosystem habitat, carbon dioxide efflux, stream temperature, water transit times, and legal frameworks that define river corridors (e.g., Acuña et al, 2005;Allen and Pavelsky, 2018;Arismendi et al, 2017;van Meerveld et al, 2019;Acuña et al, 2014;Ward et al, 2018).…”