Studying the hysteretic relationships embedded in high‐frequency suspended‐sediment concentration and river discharge data over 600+ storm events provides insight into the drivers and sources of riverine sediment during storm events. However, the literature to date remains limited to a simple visual classification system (linear, clockwise, counter‐clockwise, and figure‐eight patterns) or the collapse of hysteresis patterns to an index. This study leverages 3 years of suspended‐sediment and discharge data to show proof‐of‐concept for automating the classification and assessment of event sediment dynamics using machine learning. Across all catchment sites, 600+ storm events were captured and classified into 14 hysteresis patterns. Event classification was automated using a restricted Boltzmann machine (RBM), a type of artificial neural network, trained on 2‐D images of the suspended‐sediment discharge (hysteresis) plots. Expansion of the hysteresis patterns to 14 classes allowed for new insight into drivers of the sediment‐discharge event dynamics including spatial scale, antecedent conditions, hydrology, and rainfall. The probabilistic RBM correctly classified hysteresis patterns (to the exact class or next most similar class) 70% of the time. With increased availability of high‐frequency sensor data, this approach can be used to inform watershed management efforts to identify sediment sources and reduce fine sediment export.

Streambank movement is an integral part of geomorphic changes along river corridors and affects a range of physical, ecological, and socio-economic systems including aquatic habitat, water quality, and infrastructure. Various methods have been used to quantify streambank erosion, including bank pins, ground surveys, lidar, and analytical models, however, due to high-cost or labour intensive fieldwork these are typically feasible or appropriate only for site-specific studies.Advancements in unmanned aircraft systems (UAS) and photogrammetry provide opportunities for more rapid and economical quantification of streambank erosion and deposition at variable scales. This work assesses the performance of UAS-based photogrammetry for capturing topography of streambank surfaces and quantifying bank movement. UAS data are compared to terrestrial laser scanner (TLS) and GPS surveying from streambank sites located in Vermont that featured a variety of bank conditions and vegetation. Cross-sectional analysis of data from UAS and TLS revealed that the UAS reliably captured the bank surface within 0.2 m of TLS and GPS surveys across all sites during leaf-off conditions. Mean error between UAS and TLS was only 0.11 m in early spring conditions. Dense summer vegetation resulted in decreased accuracy and was a limiting factor in the ability of the UAS to capture the ground surface. At areas with observed bank movement, the change in cross-sectional area estimated using UAS data compared reliably to TLS survey for net cross-sectional changes greater than 3.5 m 2 , given a 10% error tolerance. At locations with smaller changes, error increased due to the effect of vegetation, georeferencing, and overhanging bank profiles. UAS-based photogrammetry shows significant promise for capturing bank topography and movement at fine resolutions in a flexible and efficient manner. KEYWORDS channel change, fluvial studies, photogrammetry, streambank erosion, terrestrial laser scanning (TLS), unmanned aircraft systems (UAS)

Given the variable biogeochemical, physical, and hydrological processes driving fluvial sediment and nutrient export, the water science and management communities need data‐driven methods to identify regions prone to production and transport under variable hydrometeorological conditions. We use Bayesian analysis to segment concentration‐discharge linear regression models for total suspended solids (TSS) and particulate and dissolved phosphorus (PP, DP) using 22 years of monitoring data from 18 Lake Champlain watersheds. Bayesian inference was leveraged to estimate segmented regression model parameters and identify threshold position. The identified threshold positions demonstrated a considerable range below and above the median discharge—which has been used previously as the default breakpoint in segmented regression models to discern differences between pre and post‐threshold export regimes. We then applied a Self‐Organizing Map (SOM), which partitioned the watersheds into clusters of TSS, PP, and DP export regimes using watershed characteristics, as well as Bayesian regression intercepts and slopes. A SOM defined two clusters of high‐flux basins, one where PP flux was predominantly episodic and hydrologically driven; and another in which the sediment and nutrient sourcing and mobilization were more bimodal, resulting from both hydrologic processes at post‐threshold discharges and reactive processes (e.g., nutrient cycling or lateral/vertical exchanges of fine sediment) at prethreshold discharges. A separate DP SOM defined two high‐flux clusters exhibiting a bimodal concentration‐discharge response, but driven by differing land use. Our novel framework shows promise as a tool with broad management application that provides insights into landscape drivers of riverine solute and sediment export.

[1] A thermodynamically consistent framework is proposed for modeling the hysteresis of capillarity in partially saturated porous media. Capillary hysteresis is viewed as an intrinsic dissipation mechanism, which can be characterized by a set of internal state variables. The volume fractions of pore fluids are assumed to be additively decomposed into a reversible part and an irreversible part. The irreversible part of the volumetric moisture content is introduced as one of the internal variables. It is shown that the pumping effect occurring in a porous medium experiencing a wetting/drying cycle is thermodynamically admissible. A generic evolution equation for internal variables is developed. By virtue of the notion of the bounding surface plasticity, a model of capillary hysteresis is developed, which is capable of predicting all types of (primary, secondary, and higher-order) scanning curves within the boundary loop. Provided that the main wetting curve and the main drying curve have been experimentally determined, the proposed model requires only one additional parameter to describe all the scanning curves. The model predictions are compared with experimental measurements found in the literature, showing that the new model is capable of describing the capillary hysteretic phenomena in a variety of partially saturated porous materials.

Nacre, a composite made from biogenic aragonite and proteins, exhibits excellent strength and toughness. Here, we show that nacreous sections can exhibit complete brittle fracture along the tablet interfaces at the proportional limit under pure shear stresses of torsion. We quantitatively separate the initial tablet sliding primarily resisted by nanoscale aragonite pillars from the following sliding resisted by various microscale toughening mechanisms. We postulate that the ductility of nacre can be limited by eliminating tablet interactions during crack propagations. Our findings should help pursuing further insights of layered materials by using torsion.

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Excessive streambank erosion is a significant source of fine sediments and associated nutrients in many river systems as well as poses risk to infrastructure. Geomorphic change detection using high-resolution topographic data is a useful method for monitoring the extent of bank erosion along river corridors. Recent advances in an unmanned aircraft system (UAS) and structure from motion (SfM) photogrammetry techniques allow acquisition of high-resolution topographic data, which are the methods used in this study. To evaluate the effectiveness of UAS-based photogrammetry for monitoring bank erosion, a fixed-wing UAS was deployed to survey 20 km of river corridors in central Vermont, in the northeastern United States multiple times over a two-year period. Digital elevation models (DEMs) and DEMs of difference allowed quantification of volumetric changes along selected portions of the survey area where notable erosion occurred. Results showed that UAS was capable of collecting high-quality topographic data at fine resolutions even along vegetated river corridors provided that the surveys were conducted in early spring, after snowmelt but prior to summer vegetation growth. Longer term estimates of streambank movements using the UAS showed good comparison to previously collected airborne lidar surveys and allowed reliable quantification of significant geomorphic changes along rivers.