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Moving down a hillslope from ridge to valley, soil develops and becomes increasingly weathered. Downslope variation in clay content, organic matter, and porosity should produce concomitant changes in soil strength that influence slope stability and erosion. This has yet to be demonstrated, however, because in situ measurements of soil rheology are challenging and rare. Here we employ a robotic leg as a mechanically sensitive and time‐efficient penetrometer to map soil strength along a canonical temperate hillslope profile. We observe a systematic downslope weakening, and increasing heterogeneity, of soil strength associated with a transition from sand‐rich ridge materials to cohesive valley bottom soil aggregates. Weathering‐induced changes in soil composition lead to physically distinct mechanical behaviors in cohesive soils that depart from the behavior observed for sand. We also demonstrate the promise that legged robots may use their limbs to sense and improve mobility in complex environments, with implications for planetary exploration.

 

Abstract Wherever a loose bed of sand is subject to sufficiently strong winds, aeolian dunes form at predictable wavelengths and growth rates. As dunes mature and coarsen, however, their growth trajectories become more idiosyncratic; nonlinear effects, sediment supply, wind variability and geologic constraints become increasingly relevant, resulting in complex and history-dependent dune amalgamations. Here we examine a fundamental question: do aeolian dunes stop growing and, if so, what determines their ultimate size? Earth’s major sand seas are populated by giant sand dunes, evolved over tens of thousands of years. We perform a global analysis of the topography of these giant dunes, and their associated atmospheric forcings and geologic constraints, and we perform numerical experiments to gain insight on temporal evolution of dune growth. We find no evidence of a previously proposed limit to dune size by atmospheric boundary layer height. Rather, our findings indicate that dunes may grow indefinitely in principle; but growth depends on morphology, slows with increasing size, and may ultimately be limited by sand supply. 

Debris flows are dense and fast-moving complex suspensions of soil and water that threaten lives and infrastructure. Assessing the hazard potential of debris flows requires predicting yield and flow behavior. Reported measurements of rheology for debris flow slurries are highly variable and sometimes contradictory due to heterogeneity in particle composition and volume fraction ( ϕ ) and also inconsistent measurement methods. Here we examine the composition and flow behavior of source materials that formed the postwildfire debris flows in Montecito, CA, in 2018, for a wide range of ϕ that encapsulates debris flow formation by overland flow. We find that shear viscosity and yield stress are controlled by the distance from jamming, Δ ϕ = ϕ m − ϕ , where the jamming fraction ϕ m is a material parameter that depends on grain size polydispersity and friction. By rescaling shear and viscous stresses to account for these effects, the data collapse onto a simple nondimensional flow curve indicative of a Bingham plastic (viscoplastic) fluid. Given the highly nonlinear dependence of rheology on Δ ϕ , our findings suggest that determining the jamming fraction for natural materials will significantly improve flow models for geophysical suspensions such as hyperconcentrated flows and debris flows. 

Sand seas are vast expanses of Earth’s surface containing large areas of aeolian dunes—topographic patterns manifest from above-threshold winds and a supply of loose sand. Predictions of the role of future climate change for sand-sea activity are sparse and contradictory. Here we examine the impact of climate on all of Earth’s presently-unvegetated sand seas, using ensemble runs of an Earth System Model for historical and future Shared Socioeconomic Pathway (SSP) scenarios. We find that almost all of the sand seas decrease in activity relative to present-day and industrial-onset for all future SSP scenarios, largely due to more intermittent sand-transport events. An increase in event wait-times and decrease in sand transport is conducive to vegetation growth. We expect dune-forming winds will become more unimodal, and produce larger incipient wavelengths, due to weaker and more seasonal winds. Our results indicate that these qualitative changes in Earth’s deserts cannot be mitigated.

 

In the midst of the COVID-19 pandemic, many live musical activities had to be postponed and even canceled to protect musicians and the audience. Orchestral ensembles face a particular challenge of contamination, because they are personally heavy and instrumentally diverse. A chief concern is whether wind instruments are vectors of contamination through aerosol dispersion. This study, made possible by the participation of members of The Philadelphia Orchestra, brings insight into the modes of production and early life of aerosols of human origin emitted by wind instruments. We find that these instruments produce aerosol levels that are comparable to normal speech in quantity and size distribution. However, the exit jet flow speeds are much lower than violent expiratory events (coughing and sneezing). For most wind instruments, the flow decays to background indoor-air levels at approximately 2 m away from the instrument's opening. Long range aerosol dispersion is, thus, via ambient air currents. 

Mud is a suspension of fine-grained particles (sand, silt, and clay) in water. The interaction of clay minerals in mud gives rise to complex rheological behaviors, such as yield stress, thixotropy, and viscoelasticity. Here, we experimentally examine the flow behaviors of kaolinite clay suspensions, a model mud, using steady shear rheometry. The flow curves exhibit both yield stress and rheological hysteresis behaviors for various kaolinite volume fractions (ϕk). Further understanding of these behaviors requires fitting to existing constitutive models, which is challenging due to numerous fitting parameters. To this end, we employ a Bayesian inference method, Markov chain Monte Carlo, to fit the experimental flow curves to a microstructural viscoelastic model. The method allows us to estimate the rheological properties of the clay suspensions, such as viscosity, yield stress, and relaxation time scales. The comparison of the inherent relaxation time scales suggests that kaolinite clay suspensions are strongly viscoelastic and weakly thixotropic at relatively low ϕk, while being almost inelastic and purely thixotropic at high ϕk. Overall, our results provide a framework for predictive model fitting to elucidate the rheological behaviors of natural materials and other structured fluids.

 

Climate change has increased the frequency and severity of extreme weather events such as hurricanes and winter storms. The complex interplay of floods with tides, runoff, and sediment creates additional hazards – including erosion and the undermining of urban infrastructure – consequently impacting the health of our rivers and ecosystems. Observations of these underwater phenomena are rare, because satellites and sensors mounted on aerial vehicles cannot penetrate the murky waters. Autonomous Surface Vehicles (ASVs) provides a means to track and map these complex and dynamic underwater phenomena. This work highlights preliminary results of high-resolution data gathering with ASVs, equipped with a suite of sensors capable of measuring physical and chemical parameters of the river. Measurements were acquired along the lower Schuylkill River in the Philadelphia area at high-tide and low-tide conditions. The data will be leveraged to improve our understanding of changes in bathymetry due to floods; the dynamics of mixing and stagnation zones and their impact on water quality; and the dynamics of suspension and resuspension of fine sediment. The data will also provide insight into the development of adaptive sampling strategies for ASVs that can maximize the information gain for future field experiments. 

Abstract How do scientists generate and weight candidate queries for hypothesis testing, and how does learning from observations or experimental data impact query selection? Field sciences offer a compelling context to ask these questions because query selection and adaptation involves consideration of the spatiotemporal arrangement of data, and therefore closely parallels classic search and foraging behavior. Here we conduct a novel simulated data foraging study—and a complementary real-world case study—to determine how spatiotemporal data collection decisions are made in field sciences, and how search is adapted in response to in-situ data. Expert geoscientists evaluated a hypothesis by collecting environmental data using a mobile robot. At any point, participants were able to stop the robot and change their search strategy or make a conclusion about the hypothesis. We identified spatiotemporal reasoning heuristics, to which scientists strongly anchored, displaying limited adaptation to new data. We analyzed two key decision factors: variable-space coverage, and fitting error to the hypothesis. We found that, despite varied search strategies, the majority of scientists made a conclusion as the fitting error converged. Scientists who made premature conclusions, due to insufficient variable-space coverage or before the fitting error stabilized, were more prone to incorrect conclusions. We found that novice undergraduates used the same heuristics as expert geoscientists in a simplified version of the scenario. We believe the findings from this study could be used to improve field science training in data foraging, and aid in the development of technologies to support data collection decisions. 

One of the simplest questions in riverine science remains unanswered: “What determines the width of rivers?” While myriad environmental and geological factors have been proposed to control alluvial river size, no accepted theory exists to explain this fundamental characteristic of river systems. We combine analysis of a global dataset with a field study to support a simple hypothesis: River geometry adjusts to the threshold fluid entrainment stress of the most resistant material lining the channel. In addition, we demonstrate how changes in bank strength dictate planform morphology by exerting strong control on channel width. Our findings greatly extend the applicability of threshold channel theory, which was originally developed to explain straight gravel-bedded rivers with uniform grain size and stable banks. The parsimonious threshold-limiting channel model describes the average hydraulic state of natural rivers across a wide range of conditions and may find use in river management, stratigraphy, and planetary science.