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Abstract. Watershed-scale stream temperature models are often one-dimensional because they require fewer data and are more computationally efficient than two- or three-dimensional models. However, one-dimensional models assume completely mixed reaches and ignore small-scale spatial temperature variability, which may create temperature barriers or refugia for cold-water aquatic species. Fine spatial- and temporal-resolution stream temperature monitoring provides information to identify river features with increased thermal variability. We used distributed temperature sensing (DTS) to observe small-scale stream temperature variability, measured as a temperature range through space and time, within two 400 m reaches in summer 2015 in Nevada's East Walker and main stem Walker rivers. Thermal infrared (TIR) aerial imagery collected in summer 2012 quantified the spatial temperature variability throughout the Walker Basin. We coupled both types of high-resolution measured data with simulated stream temperatures to corroborate model results and estimate the spatial distribution of thermal refugia for Lahontan cutthroat trout and other cold-water species. Temperature model estimates were within the DTS-measured temperature ranges 21 % and 70 % of the time for the East Walker River and main stem Walker River, respectively, and within TIR-measured temperatures 17 %, 5 %, and 5 % of the time for the East Walker, West Walker, and main stem Walker rivers, respectively. DTS, TIR, and modeled stream temperatures in the main stem Walker River nearly always exceeded the 21 ∘C optimal temperature threshold for adult trout, usually exceeded the 24 ∘C stress threshold, and could exceed the 28 ∘C lethal threshold for Lahontan cutthroat trout. Measured stream temperature ranges bracketed ambient river temperatures by −10.1 to +2.3 ∘C in agricultural return flows, −1.2 to +4 ∘C at diversions, −5.1 to +2 ∘C in beaver dams, and −4.2 to 0 ∘C at seeps. To better understand the role of these river features on thermal refugia during warm time periods, the respective temperature ranges were added to simulated stream temperatures at each of the identified river features. Based on this analysis, the average distance between thermal refugia in this system was 2.8 km. While simulated stream temperatures are often too warm to support Lahontan cutthroat trout and other cold-water species, thermal refugia may exist to improve habitat connectivity and facilitate trout movement between spawning and summer habitats. Overall, high-resolution DTS and TIR measurements quantify temperature ranges of refugia and augment process-based modeling. 

Photomineralization, the transformation of dissolved organic carbon (DOC) to CO 2 by sunlight, is an important source of CO 2 in arctic surface waters. However, quantifying the role of photomineralization in inland waters is limited by the understanding of hydrologic controls on this process. To bridge this gap, this study evaluates mixing limitations, i.e. , whether and by how much vertical mixing limits the depth-integrated photomineralization rate, in freshwater systems. We developed a conceptual model to qualitatively assess mixing limitations across the range of light attenuation and hydrologic conditions observed in freshwaters. For the common case of exponential light attenuation over depth, we developed a mathematical model to quantify mixing limitation, and used this model to assess a range of arctic freshwater systems. The results demonstrate that mixing limitations are important when there is significant light attenuation by suspended sediment (SS), which is the case in some arctic, boreal and temperate waters. Mixing limitation is pronounced when light attenuation over depth is strong and when the photomineralization rate at the water surface exceeds the vertical mixing rate. Arctic streams and rivers have strong vertical mixing relative to surface photomineralization, such that model results demonstrate no mixing limitation regardless of how much SS is present. Our analysis indicates that well-mixed assumptions used in prior work are valid in many, but not all, arctic surface waters. The effects of mixing limitations in reducing the photomineralization rate must be considered in arctic lakes with high SS concentrations. 

Abstract The Logan River watershed, located in Northern Utah, USA, consists of a relatively pristine, mountainous area that drains to a lower elevation, valley area influenced by both urban development and agriculture. The Logan River Observatory has been collecting aquatic (streamflow and water quality) and climate data throughout the Logan River watershed since 2014. While streamflow measurements are commonly made at the outlets of research watersheds, the Logan River watershed consists of diverse hydrologic, topographic, and geologic settings that require a detailed understanding of streamflow variability over time at many locations. Here, we illustrate: (a) the importance of collecting streamflow time series throughout complex watersheds, and (b) how simple flow balances can provide much needed hydrologic insight into the locations and timing of gains and losses over reaches to guide future investigations. 

Abstract Snow dominated mountainous karst watersheds are the primary source of water supply in many areas in the western U.S. and worldwide. These watersheds are typically characterized by complex terrain, spatiotemporally varying snow accumulation and melt processes, and duality of flow and storage dynamics because of the juxtaposition of matrix (micropores and small fissures) and karst conduits. As a result, predicting streamflow from meteorological inputs has been challenging due to the inability of physically based or conceptual hydrologic models to represent these unique characteristics. We present a hybrid modeling approach that integrates a physically based, spatially distributed, snow model with a deep learning karst model. More specifically, the high‐resolution snow model captures spatiotemporal variability in snowmelt, and the deep learning model simulates the corresponding response of streamflow as influenced by complex surface and subsurface properties. The deep learning model is based on the Convolutional Long Short‐Term Memory (ConvLSTM) architecture capable of handling spatiotemporal recharge patterns and watershed storage dynamics. The hybrid modeling approach is tested on a watershed in northern Utah with seasonal snow cover and variably karstified carbonate bedrock. The hybrid models were able to simulate streamflow at the watershed outlet with high accuracy. The spatial and temporal recharge and discharge patterns learned by the ConvLSTM model were then examined and compared with known hydrogeologic information. Results suggest that ConvLSTM simulates streamflow with higher accuracy than reference models for the study area and provides insight into spatially influenced hydrologic responses that are unavailable within lumped modeling approaches. 

Abstract Hyporheic exchange has the potential to significantly influence river temperatures in regions of continuous permafrost under low‐flow conditions given the strong thermal gradients that exist in river bed sediments. However, there is limited understanding of the impacts of hyporheic exchange on Arctic river temperatures. To address this knowledge gap, heat fluxes associated with hyporheic exchange were estimated in a fourth‐order Arctic river using field observations coupled with a river temperature model that accounts for hyporheic exchange influences. Temperature time series and tracer study solute breakthrough curves were measured in the main channel and river bed at multiple locations and depths to characterize hyporheic exchange and provide parameter bounds for model calibration. Model results for low‐flow periods from 3 years indicated that hyporheic exchange contributed up to 27% of the total river energy balance, reduced the main channel diel temperature range by up to 1.7 °C, and reduced mean daily temperatures by up to 0.21 °C over a 13.1‐km study reach. These influences are due to main channel heat loss during the day and gain at night via hyporheic exchange and heat loss from the hyporheic zone to the ground below via conduction. Main channel temperatures were found to be sensitive to simulated changes in ground temperatures due to changes in hyporheic exchange heat flux and deeper ground conduction. These results suggest that the moderating influence of hyporheic exchange could be reduced if ground temperatures warm in response to projected increases in permafrost thaw below rivers.