Description: Introduction Medical errors have an incidence of 9% and may lead to worse patient outcome. Teamwork training has the capacity to significantly reduce medical errors and therefore improve patient outcome. One common framework for teamwork training is crisis resource management, adapted from aviation and usually trained in simulation settings. Debriefing after simulation is thought to be crucial to learning teamwork-related concepts and behaviours but it remains unclear how best to debrief these aspects. Furthermore, teamwork-training sessions and studies examining education effects on undergraduates are rare. The study aims to evaluate the effects of two teamwork-focused debriefings on team performance after an extensive medical student teamwork training. Methods and analyses A prospective experimental study has been designed to compare a well-established three-phase debriefing method (gather–analyse–summarise; the GAS method ) to a newly developed and more structured debriefing approach that extends the GAS method with TeamTAG (teamwork techniques analysis grid). TeamTAG is a cognitive aid listing preselected teamwork principles and descriptions of behavioural anchors that serve as observable patterns of teamwork and is supposed to help structure teamwork-focused debriefing. Both debriefing methods will be tested during an emergency room teamwork-training simulation comprising six emergency medicine cases faced by 35 final-year medical students in teams of five. Teams will be randomised into the two debriefing conditions. Team performance during simulation and the number of principles discussed during debriefing will be evaluated. Learning opportunities, helpfulness and feasibility will be rated by participants and instructors. Analyses will include descriptive, inferential and explorative statistics. Ethics and dissemination The study protocol was approved by the institutional office for data protection and the ethics committee of Charité Medical School Berlin and registered under EA2/172/16. All students will participate voluntarily and will sign an informed consent after receiving written and oral information about the study. Results will be published.

Description: With the Earth’s climate rapidly warming, the Arctic represents one of the most vulnerable regions to environmental change. Permafrost, as a key element of the Arctic system, stores vast amounts of organic carbon that can be microbially decomposed into the greenhouse gases CO2 and CH4 upon thaw. Extensive thawing of these permafrost soils therefore has potentially substantial consequences to greenhouse gas concentrations in the atmosphere. In addition, thaw of ice-rich permafrost lastingly alters the surface topography and thus the hydrology. Fires represent an important disturbance in boreal permafrost regions and increasingly also in tundra regions as they combust the vegetation and upper organic soil layers that usually provide protective insulation to the permafrost below. Field studies and local remote sensing studies suggest that fire disturbances may trigger rapid permafrost thaw, with consequences often already observable in the first years post-disturbance. In polygonal ice-wedge landscapes, this becomes most prevalent through melting ice wedges and degrading troughs. The further these ice wedges degrade; the more troughs will likely connect and build an extensive hydrological network with changing patterns and degrees of connectivity that influences hydrology and runoff throughout large regions. While subsiding troughs over melting ice wedges may host new ponds, an increasing connectivity may also subsequently lead to more drainage of ponds, which in turn can limit further thaw and help stabilize the landscape. Whereas fire disturbances may accelerate the initiation of this process, the general warming of permafrost observed across the Arctic will eventually result in widespread degradation of polygonal landscapes. To quantify the changes in such dynamic landscapes over large regions, remote sensing data offers a valuable resource. However, considering the vast and ever-growing volumes of Earth observation data available, highly automated methods are needed that allow extracting information on the geomorphic state and changes over time of ice-wedge trough networks. In this study, we investigate these changing landscapes and their environmental implications in fire scars in Northern and Western Alaska. We developed a computer vision algorithm to automatically extract ice-wedge polygonal networks and the microtopography of the degrading troughs from high-resolution, airborne laserscanning-based digital terrain models (1 m spatial resolution; full-waveform Riegl Q680i LiDAR sensor). To derive information on the availability of surface water, we used optical and near-infrared aerial imagery at spatial resolutions of up to 5 cm captured by the Modular Aerial Camera System (MACS) developed by DLR. We represent the networks as graphs (a concept from the computer sciences to describe complex networks) and apply methods from graph theory to describe and quantify hydrological network characteristics of the changing landscape. Due to a lack of historical very-high-resolution data, we cannot investigate a dense time series of a single representative study area on the evolution of the microtopographic and hydrologic network, but rather leverage the possibilities of a space-for-time substitution. We thus investigate terrain models and multispectral data from 2019 and 2021 of ten study areas located in ten fire scars of different ages (up to 120 years between date of disturbance and date of data acquisition). With this approach, we can infer past and future states of degradation from the currently prevailing spatial patterns and show how this type of disturbed landscape evolves over time. Representing such polygonal landscapes as graphs and reducing large amounts of data into few quantifiable metrics, supports integration of results into i.e., numerical models and thus largely facilitates the understanding of the underlying complex processes of GHG emissions from permafrost thaw. We highlight these extensive possibilities but also illustrate the limitations encountered in the study that stem from a reduced availability and accessibility to pan-Arctic very-high-resolution Earth observation datasets.

Description: Remote sensing-based Earth Observation plays an important role in assessing environmental changes throughout our planet. As an image-heavy domain, the evaluation of the data strongly focuses on statistical and pixel-based spatial analysis methods. However, considering the complexity of our Earth system, there are some environmental structures and dependencies that are not possible to accurately describe with these traditional image analysis approaches. One example for such a limitation is the representation of (spatial) networks and their characteristics. In this study, we thus propose a computer vision approach that enables the representation of semantic information gained from images as graphs. As an example, we investigate digital terrain models of Arctic permafrost landscapes with its very characteristic polygonal patterned ground. These regular patterns, which are clearly visible in high-resolution image and elevation data, are formed by subsurface ice bodies that are very vulnerable to rising temperatures in a warming Arctic. Observing these networks’ topologies and metrics in space and time with graph analysis thus allows insights into the landscape’s complex geomorphology, hydrology, and ecology and therefore helps to quantify how they interact with climate change. We show that results extracted with this analytical and highly automated approach are in line with those gathered from other manual studies or from manual validation. Thus, with this approach, we introduce a method that, for the first time, enables upscaling of such terrain and network analysis to potentially pan-Arctic scales where collecting in-situ field data is strongly limited.

Description: Abstract In response to increasing temperatures and precipitation in the Arctic, ice-rich permafrost landscapes are undergoing rapid changes. In permafrost lowland landscapes, polygonal ice wedges are especially vulnerable, and their melting induces widespread subsidence triggering the transition from low-centered (LCP) to high-centered polygons (HCP) by forming degrading troughs. This process has an important impact on surface hydrology, as the connectivity of such trough networks determines the rate of drainage of an entire landscape (Liljedahl et al., 2016). While scientists have observed this degradation trend throughout large domains in the polygonal patterned Arctic landscape over timescales of multiple decades, it is especially evident in disturbed areas such as fire scars (Jones et al., 2015). Here, wildfires removed the insulating organic soil layer. We can therefore observe the LCP-to-HCP transition within only several years. Until now, studies on quantifying trough connectivity have been limited to local field studies and sparse time series only. With high-resolution Earth observation data, a more comprehensive analysis is possible. However, when considering the vast and ever-growing volumes of data generated, highly automated and scalable methods are needed that allow scientists to extract information on the geomorphic state and on changes over time of ice-wedge trough networks. In this study, we combine very-high-resolution (VHR) aerial imagery and comprehensive databases of segmented polygons derived from VHR optical satellite imagery (Witharana et al., 2018) to investigate the changing polygonal ground landscapes and their environmental implications in fire scars in Northern and Western Alaska. Leveraging the automated and scalable nature of our recently introduced approach (Rettelbach et al., 2021), we represent the polygon networks as graphs (a concept from computer science to describe complex networks) and use graph metrics to describe the state of these (hydrological) trough networks. Due to a lack of historical data, we cannot investigate a dense time series of a single representative study area on the evolution of the network, but rather leverage the possibilities of a space-for-time substitution. Thus, we focus on data from multiple fire scars of different ages (up to 120 years between date of disturbance and date of acquisition). With our approach, we might infer past and future states of degradation from the currently prevailing spatial patterns showing how this type of disturbed landscape evolves over space and time. It further allows scientists to gain insights into the complex geomorphology, hydrology, and ecology of landscapes, thus helping to quantify how they interact with climate change.

Description: In the face of a warming Arctic, ice-rich permafrost landscapes are undergoing rapid changes. Ice-wedge polygonal networks in Arctic lowlands are especially vulnerable and melting ice wedges can induce widespread subsidence and trough formation. The transition from low-centered to high-centered polygons can have important implications on surface hydrology, as the connectivity of the newly forming trough networks determines the rate of drainage for these lowland landscapes. However, quantifying such dynamics can be challenging, as even small-scale changes can have far-reaching implications for the larger scale hydrology of a region. In this talk, we introduce an automated workflow that enables quantification of trough network dynamics in thaw-affected landscapes. We use methods from traditional computer vision to extract (a) the spatial pattern of the trough network and (b) the morphological parameters of trough width and depth from high-resolution digital terrain models. Finally, we (c) incorporate this information into graphs - a mathematical concept used to represent complex networks - and use graph analysis methods to determine progressing subsidence and trough formation. Based on a study area in the Anaktuvuk River Fire scar on the North Slope, Alaska, USA, we present the potentials and benefits of such graph algorithms for quantifying the erosional development of this thaw-affected landscape. In our study region, we observed an increase (+127%) in the number of discernible troughs as well as their connectivity (number of disconnected networks decreased by 89%) over the observed period of ten years. The average width of troughs has increased (+14.5%), while the depth has decreased (-12.5%). With this approach, for the first time, a large-scale analysis of such detailed ground-ice and hydrological surface dynamics is made possible.