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  1. Marco Borga ; Francesco Avanzi (Ed.)
    Free, publicly-accessible full text available April 1, 2024
  2. Abstract

    Models developed to capture underlying river processes over long historical periods and varying hydrologic conditions provide confidence for subsequent forecasting applications. However, many areas lack the weather data needed to develop process‐based models over these long periods. Climate reanalysis data sets (CRDs) are increasingly used as surrogates for historical meteorology, but their use in river temperature models is still relatively new and untested. Testing of temperature models using CRDs in rivers experiencing a range of instream flow, weather, and topographic conditions is needed to validate the application of these data sets. Focusing on the ERA5‐Land CRD, correction methods that relate weather variables and elevation were tested using weather stations surrounding and adjacent to the Colorado River in Grand Canyon. Our findings show that elevation corrections improved air temperature and relative humidity, but negatively impacted wind speed estimates. Two‐year river temperature model simulations in a 387‐km segment of the Colorado River in Grand Canyon and a 576‐km segment of the Green River showed that using elevation‐corrected ERA5‐Land inputs produced lower mean errors at downstream river locations when compared to predictions using elevation‐corrected ground‐based inputs. Better river temperature predictions when using ERA5‐Land are attributed to the ability to represent spatial variability in weather conditions over these large areas. These promising results persisted when spatially coarsened ERA5‐Land inputs were used. This study highlights the importance of having spatially varying weather information, even at relatively coarse resolutions, when modeling physical processes over large spatial scales and suggests confidence in using CRDs for obtaining this information.

     
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  3. 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. 
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  4. 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.

     
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  5. Abstract

    Streams in semi‐arid urban and agricultural environments are often heavily diverted for anthropogenic purposes. However, they simultaneously receive substantial inflows from a variety of ungaged sources including stormwater returns, tile drainage, and irrigation runoff that help sustain flow during dry periods. Due to the inability to identify sources or directly gage many of these inflows, there is a clear need for methods to understand source origination while quantifying potential gains and losses over highly impacted reaches. In the context of the Logan River Observatory, historical gage data illustrate the importance of ungaged and unidentified inflows on maintaining or enhancing flows in both urban and agricultural reaches containing large diversions. To understand the inflows in this portion of the Logan River, we first analysed water samples for ions collected from a subset of representative inflow sources and applied clustering analyses to establish inflow source classifications and associated ion concentration ranges. These representative concentration ranges, combined with mainstem flow and river ion samples taken at sub‐reach scales, allow for the application of flow and mass balances to quantify inflow rates from different sources as well as any losses. These calculations demonstrate significant gains and losses occurring in many sub‐reaches during three sampling events. The dominant land use (urban or agriculture) and flow regime at the time of sampling were the primary drivers of gains and losses. These exchanges were found to be most important below large diversions during low flow conditions. This highlights the need to classify inflow sources (urban or agriculture, surface or groundwater) and estimate their contributions to anticipate instream consequences of land use and water management decisions. As irrigation and water conveyance practices become more efficient, a portion of these ungaged inflows could be diminished or eliminated, thus further depleting streamflow during dry periods.

     
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  6. 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. 
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  7. Abstract

    Hydropeaking, the alternating storage and release of water from reservoirs for hydropower generation, perturbs the thermal regime of many large rivers. While its effects on river temperature have been long studied, impacts on the thermal regime of riverbeds remain mostly unknown, despite riverbed temperature affecting rates of nutrient cycling and habitat suitability for benthic organisms. This study combines detailed field observations and flow and heat transport modeling to assess the impact of hydropeaking on riverbed temperatures in a large regulated river. The field site was 12 km downstream from a dam that induces large daily flow variations. Vertical thermistor arrays were used to collect riverbed temperature data across the entire 70 m‐wide channel. The riverbed near the left bank was highly dynamic thermally, transitioning between river and groundwater temperatures over daily hydropeaking cycles. In contrast, the rest of the riverbed, including near the right bank, was similar in temperature to the river and had relatively stable temperatures. Modeling showed that the temperatures near the banks are explained by advective heat transport driven by hydrostatic changes in river level, while the temperatures over the rest of the channel can be explained mostly by conductive heating. Gaining groundwater conditions and high sediment hydraulic conductivity favor thermally dynamic zones near banks, while low hydraulic conductivity (below 1 m/d) and neutral or losing groundwater conditions result in muted temperature fluctuations, as observed at the right bank. These spatial patterns can help predict thermally sensitive processes in the riverbeds of hydropeaked or flooding rivers.

     
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  8. 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.

     
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