[1] Temperature is a fundamentally important driver of ecosystem processes in streams. Recent warming of terrestrial climates around the globe has motivated concern about consequent increases in stream temperature. More specifically, observed trends of increasing air temperature and declining stream flow are widely believed to result in corresponding increases in stream temperature. Here, we examined the evidence for this using long-term stream temperature data from minimally and highly human-impacted sites located across the Pacific continental United States. Based on hypothesized climate impacts, we predicted that we should find warming trends in the maximum, mean and minimum temperatures, as well as increasing variability over time. These predictions were not fully realized. Warming trends were most prevalent in a small subset of locations with longer time series beginning in the 1950s. More recent series of observations exhibited fewer warming trends and more cooling trends in both minimally and highly human-influenced systems. Trends in variability were much less evident, regardless of the length of time series. Based on these findings, we conclude that our perspective of climate impacts on stream temperatures is clouded considerably by a lack of long-term data on minimally impacted streams, and biased spatio-temporal representation of existing time series. Overall our results highlight the need to develop more mechanistic, process-based understanding of linkages between climate change, other human impacts and stream temperature, and to deploy sensor networks that will provide better information on trends in stream temperatures in the future. Citation: Arismendi, I., S. L. Johnson, J. B.Dunham, R. Haggerty, and D. Hockman-Wert (2012), The paradox of cooling streams in a warming world: Regional climate trends do not parallel variable local trends in stream temperature in the Pacific continental United States, Geophys.

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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.

The seasonal and inter-annual variability of flow presence and water temperature within headwater streams of the Great Basin of the western United States limit the occurrence and distribution of coldwater fish and other aquatic species. To evaluate changes in flow presence and water temperature during seasonal dry periods, we developed spatial stream network (SSN) models from remotely sensed land-cover and climatic data that account for autocovariance within stream networks to predict the May to August flow presence and water temperature between 2015 and 2017 in two arid watersheds within the Great Basin: Willow and Whitehorse Creeks in southeastern Oregon and Willow and Rock Creeks in northern Nevada. The inclusion of spatial autocovariance structures improved the predictive performance of the May water temperature model when the stream networks were most connected, but only marginally improved the August water temperature model when the stream networks were most fragmented. As stream network fragmentation increased from the spring to the summer, the SSN models revealed a shift in the scale of processes affecting flow presence and water temperature from watershed-scale processes like snowmelt during high-runoff seasons to local processes like groundwater discharge during sustained seasonal dry periods.

For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment-visit https://www.usgs.gov or call 1-888-ASK-USGS.For an overview of USGS information products, including maps, imagery, and publications, visit https://store.usgs.gov.Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.Suggested citation: Heck, M.P., Schultz, L.D., Hockman-Wert, D., Dinger, E.C., and Dunham, J.B., 2018, Monitoring stream temperatures-A guide for non-specialists: U.S. Geological Survey Techniques and Methods, book 3, chap. A25, 76 p., https://doi.org/10.3133/tm3A25. Executive SummaryWater temperature influences most physical and biological processes in streams, and along with streamflows is a major driver of ecosystem processes. Collecting data to measure water temperature is therefore imperative, and relatively straightforward. Several protocols exist for collecting stream temperature data, but these are frequently directed towards specialists. This document was developed to address the need for a protocol intended for non-specialists (non-aquatic) staff. It provides specific step-by-step procedures on (1) how to launch data loggers, (2) check the factory calibration of data loggers prior to field use, (3) how to install data loggers in streams for year-round monitoring, (4) how to download and retrieve data loggers from the field, and (5) how to input project data into organizational databases. (2014), and Mauger and others (2015). Although thorough, these previous protocols lack the clear guidance for implementation for non-specialists. Our objective in this report is to provide a simplified distillation of this advice for nonspecialists who may not have experience in monitoring stream temperature, as well as providing standardized techniques and basic reporting. After reading through this protocol, non-specialists with an interest in monitoring streams and water quality will have the capability to effectively install stream temperature data loggers to remotely record water temperatures. Why Monitor Water Temperature?In streams, temperature represents the collective influence of many factors that influence heat exchanges (Caissie, 2006), including heat gains from solar radiation, inflows of groundwater or tributaries, and losses of heat from evaporation or radiation to the atmosphere. Changes in streamflows, stream shading, and other factors can significantly influence stream temperatures. Increasingly, changing precipitation patterns, decreasing snow cover and glaciers, and warming air temperatures, among other factors, have led to concerns about warming temperatures in streams (Isaak, Young, and others, 2016). Collectively, temperatures in s...

Understanding local and geographic factors influencing species distributions is a prerequisite for conservation planning. Our objective in this study was to model local and geographic variability in elevations occupied by native and nonnative trout in the northwestern Great Basin, USA. To this end, we analyzed a large existing data set of trout presence (5,156 observations) to evaluate two fundamental factors influencing occupied elevations: climaterelated gradients in geography and local constraints imposed by topography. We applied quantile regression to model upstream and downstream distribution elevation limits for each trout species commonly found in the region (two native and two nonnative species). With these models in hand, we simulated an upstream shift in elevation limits of trout distributions to evaluate potential consequences of habitat loss. Downstream elevation limits were inversely associated with latitude, reflecting regional gradients in temperature. Upstream limits were positively related to maximum stream elevation as expected. Downstream elevation limits were constrained topographically by valley bottom elevations in northern streams but not in southern streams, where limits began well above valley bottoms. Elevation limits were similar among species. Upstream shifts in elevation limits for trout would lead to more habitat loss in the north than in the south, a result attributable to differences in topography. Because downstream distributions of trout in the north extend into valley bottoms with reduced topographic relief, trout in more northerly latitudes are more likely to experience habitat loss associated with an upstream shift in lower elevation limits. By applying quantile regression to relatively simple information (species presence, elevation, geography, topography), we were able to identify elevation limits for trout in the Great Basin and explore the effects of potential shifts in these limits that could occur in response to changing climate conditions that alter streams directly (e.g., through changes in temperature and precipitation) or indirectly (e.g., through changing water use).

To investigate effects of headwater logging on downstream coastal cutthroat trout (Oncorhynchus clarkii clarkii) populations, we monitored stream habitat and biotic indicators including biomass, abundance, growth, movement, and survival over 8 years using a paired-watershed approach. Reference and logged catchments were located on private industrial forestland on ∼60-year harvest rotation. Five clearcuts (14% of the logged catchment area) were adjacent to fishless portions of the headwater streams, and contemporary regulations did not require riparian forest buffers in the treatment catchment. Logging did not have significant negative effects on downstream coastal cutthroat trout populations for the duration of the sample period. Indeed, the only statistically significant response of fish populations following logging in fishless headwaters was an increase in late-summer biomass (g·m−2) of age-1+ coastal cutthroat trout in tributaries. Ultimately, the ability to make broad generalizations concerning effects of timber harvest is difficult because response to disturbance (anthropogenically influenced or not) in aquatic systems is complex and context-dependent, but our findings provide one example of environmentally compatible commercial logging in a regenerated forest setting.