Evaluation of Continuous Monitoring as a Tool for Municipal Stormwater Management Programs

The purpose of this study is to evaluate the uncertainty attributable to inadequate temporal sampling of stormwater discharge and water quality, and understand its implications for meeting monitoring objectives relevant to municipal separate storm sewer systems (MS4s). A methodology is presented to evaluate uncertainty attributable to inadequate temporal sampling of continuous stormflow and water quality, and a case study demonstrates the application of the methodology to six small urban watersheds (0.8-6.8 km2) and six large rural watersheds (30-16,192 km2) in Virginia. Results indicate the necessity of high-frequency continuous monitoring for accurately capturing multiple monitoring objectives, including illicit discharges, acute toxicity events, and stormflow pollutant concentrations and loads, as compared to traditional methods of sampling. For example, 1-h sampling in small urban watersheds and daily sampling in large rural watersheds would introduce uncertainty in capturing pollutant loads of 3–46% and 10–28%, respectively. Overall, the outcomes from this study highlight how MS4s can leverage continuous monitoring to meet multiple objectives under current and future regulatory environments

Body Worn Cameras, use of force and police-civilian interactions: Capturing complexities, documenting the unexpected, and learning lessons

Editorial CommentThis is the author accepted manuscript. The final version is available from Oxford University Press via the DOI in this record.This special issue seeks to present a diversity of research and commentary on a promising yet highly controversial issue in policing: the use of police body-worn cameras (BWCs). [...

The student experience of applied equivalence-based instruction for neuroanatomy teaching

Purpose Esoteric jargon and technical language are potential barriers to the teaching of science and medicine. Effective teaching strategies which address these barriers are desirable. Here, we created and evaluated the effectiveness of stand-alone ‘equivalence-based instruction’ (EBI) learning resources wherein the teaching of a small number of direct relationships between stimuli (e.g., anatomical regions, their function, and pathology) results in the learning of higher numbers of untaught relationships. Methods We used a pre and post test design to assess students’ learning of the relations. Resources were evaluated by students for perceived usefulness and confidence in the topic. Three versions of the resources were designed, to explore learning parameters such as the number of stimulus classes and the number of relationships within these classes. Results We show that use of EBI resulted in demonstrable learning of material that had not been directly taught. The resources were well received by students, even when the quantity of material to be learned was high. There was a strong desire for more EBI-based teaching. The findings are discussed in the context of an ongoing debate surrounding ‘rote’ vs. ‘deep’ learning, and the need to balance this debate with considerations of cognitive load and esoteric jargon routinely encountered during the study of medicine. Conclusion These standalone EBI resources were an effective, efficient and well-received method for teaching neuroanatomy to medical students. The approach may be of benefit to other subjects with abundant technical jargon, science and other areas of medicine

A New Model For Simulating Climate Change and Carbon Dynamics in Forested Landscapes

Journal of Ecosystems & Management vol. 13 no. 2 2012 news brief

Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions

A project established at the National Institute of Water and Atmospheric Research (NIWA) in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO) Numerical Weather Prediction model (NWP) are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM) to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48~h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70–90%

Counter Machines and Distributed Automata: A Story about Exchanging Space and Time

We prove the equivalence of two classes of counter machines and one class of distributed automata. Our counter machines operate on finite words, which they read from left to right while incrementing or decrementing a fixed number of counters. The two classes differ in the extra features they offer: one allows to copy counter values, whereas the other allows to compute copyless sums of counters. Our distributed automata, on the other hand, operate on directed path graphs that represent words. All nodes of a path synchronously execute the same finite-state machine, whose state diagram must be acyclic except for self-loops, and each node receives as input the state of its direct predecessor. These devices form a subclass of linear-time one-way cellular automata.Comment: 15 pages (+ 13 pages of appendices), 5 figures; To appear in the proceedings of AUTOMATA 2018

Carbon Sequestration in Managed Temperate Coniferous Forests Under Climate Change

Management of temperate forests has the potential to increase carbon sinks and mitigate climate change. However, those opportunities may be confounded by negative climate change impacts. We therefore need a better understanding of climate change alterations to temperate forest carbon dynamics before developing mitigation strategies. The purpose of this project was to investigate the interactions of species composition, fire, management, and climate change in the Copper–Pine Creek valley, a temperate coniferous forest with a wide range of growing conditions. To do so, we used the LANDIS-II modelling framework including the new Forest Carbon Succession extension to simulate forest ecosystems under four different productivity scenarios, with and without climate change effects, until 2050. Significantly, the new extension allowed us to calculate the net sector productivity, a carbon accounting metric that integrates aboveground and belowground carbon dynamics, disturbances, and the eventual fate of forest products. The model output was validated against literature values. The results implied that the species optimum growing conditions relative to current and future conditions strongly influenced future carbon dynamics. Warmer growing conditions led to increased carbon sinks and storage in the colder and wetter ecoregions but not necessarily in the others. Climate change impacts varied among species and site conditions, and this indicates that both of these components need to be taken into account when considering climate change mitigation activities and adaptive management. The introduction of a new carbon indicator, net sector productivity, promises to be useful in assessing management effectiveness and mitigation activities