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This data release contains two debris-flow inventories summarizing observations from burned and unburned areas in the western Cascade Range of Oregon (OR). The burned inventory focuses on debris flows that occurred during the first two years after the 2020 Archie Creek, Holiday Farm, Beachie Creek/Lionshead, and Riverside fires (OR_field_observations.csv). The unburned inventory (1995-2022) focuses on debris flows in the same areas (excluding the Riverside Fire). The inventories are derived from field observations (OR_field_observations.csv) and aerial imagery (OR_imagery_observations.csv). They include mapped debris-flow initiation locations, descriptions of the inferred initiation process, other notable site characteristics, and rainfall data. Locations of debris flows observed after wildfires are also linked to USGS postfire debris-flow hazard assessments (USGS, 2022; Staley and others, 2017; Thomas and others 2023). Rainfall characteristics for each debris flow in the inventory are derived from the closest rainfall gage to an observed debris flow (gage_locations.csv). Peak rainfall rates during the known time window of debris-flow initiation are reported for durations of 15 minutes, 30 minutes, 60 minutes, 12 hours, 24 hours, 36 hours, and 48 hours. More detailed explanations of the headers for each of these csv files can be found within the README_csvname.txt file. References: Landslide Hazards Program. (n.d.). Emergency assessment of post-fire debris-flow hazards. U.S. Geological Survey. https://landslides.usgs.gov/hazards/postfire_debrisflow Staley, D. M., Negri, J. A., Kean, J. W., Laber, J. L., Tillery, A. C., and Youberg, A. M., 2017, Prediction of spatially explicit rainfall intensity–duration thresholds for post-fire debris-flow generation in the western United States. Geomorphology, 278, 149–162. https://doi.org/10.1016/j.geomorph.2016.10.019 Thomas, M. A., Kean, J. W., McCoy, S. W., Lindsay, D. N., Kostelnik, J., Cavagnaro, D. B., Rengers, F. K., East, A. E., Schwartz, J. Y., Smith, D. P., and Collins, B. D., 2023, Postfire hydrologic response along the Central California (USA) coast: insights for the emergency assessment of postfire debris-flow hazards. Landslides, 20, 2421-2436. https://doi.org/10.1007/s10346-023-02106-7 

Abstract Debris flows pose a significant hazard to communities in mountainous areas, and there is a continued need for methods to delineate hazard zones associated with debris-flow inundation. In certain situations, such as scenarios following wildfire, where there could be an abrupt increase in the likelihood and size of debris flows that necessitates a rapid hazard assessment, the computational demands of inundation models play a role in their utility. The inability to efficiently determine the downstream effects of anticipated debris-flow events remains a critical gap in our ability to understand, mitigate, and assess debris-flow hazards. To better understand the downstream effects of debris flows, we introduce a computationally efficient, reduced-complexity inundation model, which we refer to as the Progressive Debris-Flow routing and inundation model (ProDF). We calibrate ProDF against mapped inundation from five watersheds near Montecito, CA, that produced debris flows shortly after the 2017 Thomas Fire. ProDF reproduced 70% of mapped deposits across a 40 km 2 study area. While this study focuses on a series of post-wildfire debris flows, ProDF is not limited to simulating debris-flow inundation following wildfire and could be applied to any scenario where it is possible to estimate a debris-flow volume. However, given its ability to reproduce mapped debris-flow deposits downstream of the 2017 Thomas Fire burn scar, and the modest run time associated with a simulation over this 40 km 2 study area, results suggest ProDF may be particularly promising for post-wildfire hazard assessment applications. 

Abstract Reconfigurable arrays of 2D nanomaterials are essential for the realization of switchable and intelligent material systems. Using liquid crystals (LCs) as a medium represents a promising approach, in principle, to enable such control. In practice, however, this approach is hampered by the difficulty of achieving stable dispersions of nanomaterials. Here, we report on good dispersions of pristine CdSe nanoplatelets (NPLs) in LCs, and reversible, rapid control of their alignment and associated anisotropic photoluminescence, using a magnetic field. We reveal that dispersion stability is greatly enhanced using polymeric, rather than small molecule, LCs and is considerably greater in the smectic phases of the resulting systems relative to the nematic phases. Aligned composites exhibit highly polarized emission that is readily manipulated by field-realignment. Such dynamic alignment of optically-active 2D nanomaterials may enable the development of programmable materials for photonic applications and the methodology can guide designs for anisotropic nanomaterial composites for a broad set of related nanomaterials. 

Intensively managed forest plantations often require fertilization to maintain site fertility and to improve growth and yield over successive rotations. We applied urea-based “enhanced-efficiency fertilizers” (EEF) containing 0.5 atom% 15N at a rate of 224 kg N ha−1 to soils under mid-rotation black walnut (Juglans nigra L.) plantations to track the fate of applied 15N within aboveground ecosystem components during the 12-month period after application. Treatments included Agrotain Ultra (urea coated with a urease inhibitor), Arborite EC (urea coated with water-soluble boron and phosphate), Agrium ESN (polymer-coated urea), uncoated urea, and an unfertilized control. Agrotain Ultra and Arborite EC increased N concentrations of competing vegetation within one month after fertilization, while neither Agrium ESN nor uncoated urea had any effect on competing vegetation N concentrations during the experiment. Agrotain Ultra and Arborite EC increased δ15N values in leaves of crop trees above those of controls at one and two months after fertilization, respectively. By contrast, Agrium ESN and uncoated urea had no effect on δ15N values in leaves of crop trees until three months after fertilization. Fertilizer N recovery (FNR) varied among ecosystem components, with competing vegetation acting as a sink for applied nutrients. There were no significant differences in FNR for all the urea-based EEF products compared to uncoated urea. Agrium ESN was the only EEF that exhibited controlled-release activity in this study, with other fertilizers behaving similarly to uncoated urea. 

Abstract The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.