This study examines data collected with an autonomous underwater glider during a period of vigorous radiatively driven convection (RDC) and low winds in deep, unstratified Lake Superior. Conductivity, temperature and depth (CTD) measurements reveal distinct convective plumes of warm downwelling water with temperature anomalies of similar to 0.1 degrees C and width scales on the order of 10-100 m, consistent with theoretical scalings for the unstratified convective regime. Shear and temperature microstructure measurements indicate turbulent kinetic energy (TKE) dissipation (epsilon) and temperature variance dissipation rates chi(T) orders of magnitude greater in thermal plumes than laterally adjacent waters. Decay timescales of epsilon indicate highly efficient mixing is sustained throughout the night. Energetics, mixing efficiency, and constraints on convective plume scales are also discussed. These observations demonstrate that RDC can dominate vertical mixing dynamics even in deep ice-free systems, and these systems can serve as a real-scale laboratory for investigation of convective dynamics.

Seedling recruitment is an important reproductive process for sustaining riparian tree populations in arid and semi-arid environments. Riparian tree species such as cottonwoods and willows are highly adapted to the dynamic riparian environment; their seed dispersal and germination patterns are tied with climatic signals that also drive hydrology. The magnitude and timing of seasonal hydrologic components determine environmental conditions that either promote or limit seedling establishment processes. This article presents the development and testing of a potential niche model for riparian seedling recruitment. The presented Riparian Seedling Recruitment Model (RSRM) identifies spatially explicit locations of suitable habitat for seedling recruitment driven by relevant hydrophysical processes. This model extends previous seedling recruitment algorithms by accounting for seedling mortality due to scour by sediment mobilization, the reduction of potential germination sites due to existing forest canopy shade, and the incorporation of engineered channel and substrate modifications. The model is integrated into the open-source river analysis software River Architect. Potential future applications for the model include assessing seedling recruitment patterns for existing conditions, under alternative flow regimes, or for designs with topographic modifications. A canonical test channel and five scenarios with relevant hydrographic features are used to perform an implementation verification. Predictable recruitment patterns for the canonical test channel demonstrate model functionality and establish a dataset for future benchmarking. Finally, results from a site on the lower Yuba River, California are presented to illustrate the usefulness of a simplified test site given the complexity of results from real-world data.

Wildfire activity is increasing globally. The resulting smoke plumes can travel hundreds to thousands of kilometers, reflecting or scattering sunlight and depositing particles within ecosystems. Several key physical, chemical, and biological processes in lakes are controlled by factors affected by smoke. The spatial and temporal scales of lake exposure to smoke are extensive and under-recognized. We introduce the concept of the lake smoke-day, or the number of days any given lake is exposed to smoke in any given fire season, and quantify the total lake smoke-day exposure in North America from 2019 to 2021. Because smoke can be transported at continental to intercontinental scales, even regions that may not typically experience direct burning of landscapes by wildfire are at risk of smoke exposure. We found that 99.3% of North America was covered by smoke, affecting a total of 1,333,687 lakes ≥10 ha. An incredible 98.9% of lakes experienced at least 10 smoke-days a year, with 89.6% of lakes receiving over 30 lake smoke-days, and lakes in some regions experiencing up to 4 months of cumulative smoke-days. Herein we review the mechanisms through which smoke and ash can affect lakes by altering the amount and spectral composition of incoming solar radiation and depositing carbon, nutrients, or toxic compounds that could alter chemical conditions and impact biota. We develop a conceptual framework that synthesizes known and theoretical impacts of smoke on lakes to guide future research. Finally, we identify emerging research priorities that can help us better understand how lakes will be affected by smoke as wildfire activity increases due to climate change and other anthropogenic activities.

Substrate facies monitoring is critical for the understanding of fluvial geomorphologic and ecohydraulic patterns and processes. However, direct substrate measurement is time-consuming and subjected to data sparsity because of small sample, size, and limited data collections within an area of interest, which make it difficult to capture facies patterns. Most new experimental studies focus on mapping substrate based on median grain size of a specific grain size class using automatic or semiautomatic photosieving techniques. This study aimed to develop and apply a method to accurately predict size-mixture facies patterns on exposed riverbeds with minimal ground truth plots (100) using airborne lidar and machine learning. The selected testbed river was a 37.5-km stretch of the regulated lower Yuba River in California, USA, mapped at sub-meter resolution in 2017. First, we designed a grid-by-point grain size sampling method and binned grain sizes into representative mixtures, such as fine or large gravel, to assign subaerial facies labels. Second, we classified facies based on a multivariate cluster analysis. Third, we generated 15 lidar-derived topographic and spectral predictors. Six distinct size-mixture facies were identified from field data and a seventh, pure sand facies, from UAV data. A random forest predictive model with an 86% 10-fold cross-validation accuracy was applied to produce a facies map at the 1.54 m pixel scale. The detrended elevation was identified as the most important variable for predicting facies spatial patterning, followed by baseflow, wetted area proximity, and green lidar intensity. We conclude that machine learning combined with intensity lidar data is highly effective for distinguishing mixed classes of substrates. Ultimately, the new substrate mixture-binning approach also provides novel insights into the arrangement of river sediment facies patterns.

Riverine fish stranding is of significant concern due to its potentially devastating impacts on fish populations already at risk. Because stranding is dependent on a wide range of biotic and abiotic factors, it is difficult to accurately identify and parameterize fish stranding risks for various river topographies, fish species/lifestages and flow ramping scenarios. This article presents a literature review, new concepts and a novel Python3 algorithm for post-processing two-dimensional hydrodynamic numerical model results to identify spatially explicit locations where fish stranding is likely, such as but not limited to downstream of hydropeaking facilities. Compared to previous stranding algorithms, this one is novel in its use of graph theory to find optimal fish escape routes and for its embedding in the free, open-source river analysis software River Architect. Guided by biological parameter selection and supplied with two-dimensional hydrodynamic model rasters, River Architect's Stranding Risk module is suitable for characterization of existing pool stranding risks, alternative flow regime and topographic design evaluation and post-project assessment of rivers during flow recessions.

The dynamics of fish stranding have not been academically investigated within the context of physical adjustments to rivers for habitat enhancement purposes. River projects may aim to help fish populations but instead may function as attractive nuisances reducing populations because of unaccounted‐for stranding risk. This study applies a novel algorithm to predict spatially explicit, meter‐resolution fish stranding risk at a river rehabilitation site in California to address three scientific questions. Postproject disconnected wetted area predictions were validated against water surface elevation measurements and time lapse photography of flow reductions and stranding events. A comparison of preproject, final design, and postproject topographies revealed that the occurrence and severity of stranding events is highly sensitive to side‐channel topographic structure and postproject morphodynamic change. Even with moderate flows, side‐channel exits tend to close off by bars built across them via bedload transport. Implications for river management practices and river rehabilitation project design are discussed.

Radiatively-driven convection is a physical process that occurs in freshwater below the temperature of maximum density wherein volumetric heating of surface waters by solar radiation creates a diurnal, spatially distributed, destabilizing buoyancy flux that drives penetrative convection. While this process has typically been studied under ice-covered conditions, it can also occur in open water during springtime warming leading up to overturn, and in such systems, it may serve as the dominant process driving mixing of nutrients and biota. Despite the ecological significance and unique physical dynamics of radiatively-driven convection, little is understood regarding the spatial heterogeneity and three-dimensional structure of the process. The addition of wind shear also modifies radiatively-driven convection dynamics in open water conditions, yet observations have not yet been used to quantify the relative scales and importance of these separate forcings in driving mixing and turbulence. This study examines data collected with a buoyancy-driven autonomous underwater vehicle (aka glider) during a period of active radiatively-driven convection and low surface wind shear in early springtime in Lake Superior. Conductivity, temperature and depth (CTD) measurements reveal distinct convective plumes of anomalously warm downwelling water with width scales on the order of 100 m and temperature anomalies of ~0.1 °C. Shear and temperature microstructure measurements indicate turbulence kinetic energy (TKE) dissipation rates exceeding 10-8 W/kg, orders of magnitude greater than laterally adjacent waters. This is the first known observation of lateral variability in TKE dissipation rates during radiatively-driven convection. Spatially and temporally averaged TKE budgets illustrate buildup, vertical transport, and dissipation of TKE, while the ~3 hr lag between buoyancy forcing and dissipation is consistent with the Deardorff convective timescale. These observations demonstrate that radiatively-driven convection can dominate vertical mixing dynamics even in deep, open water systems.

River design is often conceptually approached aiming at either physical channel stability or ecological functionality. We present a novel concept within an open-source software called River Architect that addresses both these goals and estimates costs. River Architect is flexible for site- and application-specific characteristics, with modules for the analysis and design of habitat-enhancing and channel-stabilising feature groups. Ecological assets are assessed as a function of a novel metric that incorporates the seasonal and discharge-dependent preferred habitat area of target species. Calculations of cost estimates and ecological efficiency are illustrated by an example in a gravel-cobble-bed river.