Desert RHex Technical Report: Jornada and White Sands Trip

Researchers in a variety of fields, including aeolian science, biology, and environmental science, have already made use of stationary and mobile remote sensing equipment to increase their variety of data collection opportunities. However, due to mobility challenges, remote sensing opportunities relevant to desert environments and in particular dune fields have been limited to stationary equipment. We describe here an investigative trip to two well-studied experimental deserts in New Mexico with D-RHex, a mobile remote sensing platform oriented towards desert research. D-RHex is the latest iteration of the RHex family of robots, which are six-legged, biologically inspired, small (10kg) platforms with good mobility in a variety of rough terrains, including on inclines and over obstacles of higher than robot hip height. For more information: Kod*La

Microbiology of wind-eroded sediments: current knowledge and future research directions

Wind erosion is a threat to the sustainability and productivity of soils that takes place at local, regional, and global scales. Current estimates of the cost of wind erosion have not included the costs associated with the loss of soil biodiversity and reduced ecosystem functions. Microorganisms carried in dust are responsible for numerous critical ecosystem processes including biogeochemical cycling of nutrients, carbon storage, soil aggregation, and transformation of toxic compounds in the source soil. Currently, much of the information on microbial transport in dust has been collected at continental scales, with no comprehensive review regarding the microbial communities, particularly those associated with agricultural systems, redistributed by wind erosion processes at smaller scales including regional or field scales. Agricultural systems can contribute significantly to atmospheric dust loading and loss or redistribution of soil microorganisms are impacted in three interactive ways: (1) differential loss of certain microbial taxa depending on particle size and wind conditions, (2) through the destabilization of soil aggregates and reduction of available surfaces, and (3) through the reduction of organic matter and substrates for the remaining community. The purpose of this review is to provide an overview of dust sampling technologies, methods for microbial extraction from dust, and how abiotic, environmental, and management factors influence the dust microbiome within and among agroecosystems. The review also offers a perspective on important potential future research avenues with a focus on agroecosystems and the inclusion of the fungal component

Dust emission from crusted surfaces: Insights from field measurements and modelling

Crusted surfaces can be major sources of mineral dust emission. Quantitative understanding of dust emission from crusted surfaces is limited, because (1) theories on dust emission are not well tested for such surfaces; and (2) modelling is hampered by a lack of input data sufficient to describe the surface conditions. Combining detailed field measurements with physics-based numerical modelling, we present new insights into dust emission from crusted surfaces. Our measurements confirm that crust erodibility and dust-emission intensity can increase or decrease after previous erosion events. To support interpretation of the measurements and to test the applicability of a state-of-the-art parameterisation to simulate dust emission from crusted surfaces, we apply the dust emission scheme of Shao (2004). Saltation flux, which is input to the scheme, is approximated using the parameterisation of Kawamura (1964) and a scaling factor obtained from observations. Limitations of this approach are discussed. Our results show that the dust emission scheme is suitable to estimate dust emission from crusted surfaces if accurate input data and parameters describing the soil-surface condition are provided. The parameters were optimized for each dust event to achieve a best estimate. The variation of the resulting parameter values confirms the observed variability of dust-emission efficiency between the events and provides further evidence that it was caused by variations in crust erodibility. Our study demonstrates that available physics-based dust-emission parameterisations are able to simulate dust emissions under complicated conditions, but also that refined information on the soil-surface conditions are needed as input to the schemes.This study was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) grant KL 2932/1-1 awarded as a postdoctoral research fellowship to MK. TEG and RSVP acknowledge support from NASA grant NNX16AH13G. 15 NPW acknowledges support through funding from the Department of Interior, Bureau of Land Management. We thank Ralph Lorenz for providing pressure loggers and the Davis anemometer used on Site F. We also thank Sharalyn Peterson, Justin Van Zee, and Bradley Cooper for field and lab assistance. LPI point data were recorded using DIMA (https://jornada.nmsu.edu/monit-assess/dima). Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. The USDA is an equal opportunity provider and employer. We thank two anonymous reviewers for their positive and helpful comments.Peer ReviewedPostprint (author's final draft

Total vertical sediment flux and PM10 emissions from disturbed Chihuahuan Desert surfaces

Desert surfaces are typically stable and represent some of the longest-lived landforms on Earth. For surfaces devoid of vegetation, the evolution of a desert pavement of gravel and small stones protects the surface from erosion by wind and water and vegetation further protects the surface in arid and semi-arid rangelands. The susceptibility of the land surface to wind erosion is enhanced by mechanical damage to the desert pavement or vegetation losses resulting from fire or grazing. Despite the relatively rich literature on the effects of grazing and fire on plant community composition, land degradation, and the productivity of arid landscapes, little is known about the effects of moderate grazing or fire on the erodibility of soils in desert grasslands and shrublands. Here we investigate the effects of simulated moderate grazing, simulated livestock trampling, and of fire on the resulting wind erodibility and dust emissions of the affected soil surfaces. We surveyed 24 plots of the same size, 6 m × 0.6 m, at a research site in the northern Chihuahuan Desert including 6 plots in a shrub-grass ecotone, 12 plots in an adjacent grassland, and 6 plots in an area that had been burned by a natural wildfire 6 months earlier but had no vegetation recovery due to the time of year and drought. To evaluate the various effects of disturbances on the susceptibility of the surface to wind erosion and dust entrainment, replicates of three plots underwent different treatments including clipping, trampling, fire, and tillage. We subsequently tested each of the treated plots with a portable field wind tunnel run at 12.6 m s−1. We found that moderate grazing and fire did not result in great soil loss in desert grasslands but that shrublands were more seriously affected by grazing and fire. Total removal of vegetation and disturbance of the soil surface did result in greater than order of magnitude increases of vertical sediment flux and greater than three-fold increases of dust emissions

Context-aware modeling of neuronal morphologies

© 2014 Torben-Nielsen and De Schutter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these termsNEURONAL MORPHOLOGIES ARE PIVOTAL FOR BRAIN FUNCTIONING: physical overlap between dendrites and axons constrain the circuit topology, and the precise shape and composition of dendrites determine the integration of inputs to produce an output signal. At the same time, morphologies are highly diverse and variant. The variance, presumably, originates from neurons developing in a densely packed brain substrate where they interact (e.g., repulsion or attraction) with other actors in this substrate. However, when studying neurons their context is never part of the analysis and they are treated as if they existed in isolation. Here we argue that to fully understand neuronal morphology and its variance it is important to consider neurons in relation to each other and to other actors in the surrounding brain substrate, i.e., their context. We propose a context-aware computational framework, NeuroMaC, in which large numbers of neurons can be grown simultaneously according to growth rules expressed in terms of interactions between the developing neuron and the surrounding brain substrate. As a proof of principle, we demonstrate that by using NeuroMaC we can generate accurate virtual morphologies of distinct classes both in isolation and as part of neuronal forests. Accuracy is validated against population statistics of experimentally reconstructed morphologies. We show that context-aware generation of neurons can explain characteristics of variation. Indeed, plausible variation is an inherent property of the morphologies generated by context-aware rules. We speculate about the applicability of this framework to investigate morphologies and circuits, to classify healthy and pathological morphologies, and to generate large quantities of morphologies for large-scale modeling.Peer reviewe

Robotic Measurement of Aeolian Processes

Measurements used to study wind shear stress and turbulence, surface roughness, sand flux, and dust emissions are typically obtained from stationary instrumentation, and are thus limited spatially. They are also dependent on deployment of instrumentation for specific events and thus the are limited temporally. We have been adapting a rough-terrain legged robot capable of rapidly traversing desert terrain to serve as a semi-autonomous, reactive mobile sensory platform (RHex [1]), which would not share these limitations. We report on early trials of the robotic platform at the Jornada LTER and White Sands National Monument to test the feasibility of gathering measurements of airflow and rates of particle transport on a dune, assessing the role of roughness elements such as vegetation in modifying the wind shear stresses incident on the surface, and estimating erosion susceptibility in an arid soil. The robot not only serves as a mobile platform for science instruments; it can also perform controlled “kick tests” to locally examine soil strength. We outline a strategy for mapping soil erodibility and its controlling parameters using the unique capabilities of RHex, and the implications for understanding erosion and dust emission from complex terrain

Sparse Matrix-Based HPC Tomography

Tomographic imaging has benefited from advances in X-ray sources, detectors and optics to enable novel observations in science, engineering and medicine. These advances have come with a dramatic increase of input data in the form of faster frame rates, larger fields of view or higher resolution, so high performance solutions are currently widely used for analysis. Tomographic instruments can vary significantly from one to another, including the hardware employed for reconstruction: from single CPU workstations to large scale hybrid CPU/GPU supercomputers. Flexibility on the software interfaces and reconstruction engines are also highly valued to allow for easy development and prototyping. This paper presents a novel software framework for tomographic analysis that tackles all aforementioned requirements. The proposed solution capitalizes on the increased performance of sparse matrix-vector multiplication and exploits multi-CPU and GPU reconstruction over MPI. The solution is implemented in Python and relies on CuPy for fast GPU operators and CUDA kernel integration, and on SciPy for CPU sparse matrix computation. As opposed to previous tomography solutions that are tailor-made for specific use cases or hardware, the proposed software is designed to provide flexible, portable and high-performance operators that can be used for continuous integration at different production environments, but also for prototyping new experimental settings or for algorithmic development. The experimental results demonstrate how our implementation can even outperform state-of-the-art software packages used at advanced X-ray sources worldwide

Ground robotic measurement of aeolian processes

Models of aeolian processes rely on accurate measurements of the rates of sediment transport by wind, and careful evaluation of the environmental controls of these processes. Existing field approaches typically require intensive, event-based experiments involving dense arrays of instruments. These devices are often cumbersome and logistically difficult to set up and maintain, especially near steep or vegetated dune surfaces. Significant advances in instrumentation are needed to provide the datasets that are required to validate and improve mechanistic models of aeolian sediment transport. Recent advances in robotics show great promise for assisting and amplifying scientists’ efforts to increase the spatial and temporal resolution of many environmental measurements governing sediment transport. The emergence of cheap, agile, human-scale robotic platforms endowed with increasingly sophisticated sensor and motor suites opens up the prospect of deploying programmable, reactive sensor payloads across complex terrain in the service of aeolian science. This paper surveys the need and assesses the opportunities and challenges for amassing novel, highly resolved spatiotemporal datasets for aeolian research using partially-automated ground mobility. We review the limitations of existing measurement approaches for aeolian processes, and discuss how they may be transformed by ground-based robotic platforms, using examples from our initial field experiments. We then review how the need to traverse challenging aeolian terrains and simultaneously make high-resolution measurements of critical variables requires enhanced robotic capability. Finally, we conclude with a look to the future, in which robotic platforms may operate with increasing autonomy in harsh conditions. Besides expanding the completeness of terrestrial datasets, bringing ground-based robots to the aeolian research community may lead to unexpected discoveries that generate new hypotheses to expand the science itself. For more information: Kod*lab (http://kodlab.seas.upenn.edu/