Local-scale post-event assessments with GPS and UAV-based quick-response surveys:A pilot case from the Emilia-Romagna (Italy) coast

Coastal communities and assets are exposed to flooding and erosion hazards due to extreme storm events, which may increase in intensity due to climatological factors in the incoming future. Coastal managers are tasked with developing risk-management plans mitigating risk during all phases of the disaster cycle. This necessitates rapid, time-efficient post-event beach surveys that collect physical data in the immediate aftermath of an event. Additionally, the inclusion of local stakeholders in the assessment process via personal interviews captures the social dimension of the impact of the event. In this study, a local protocol for post-event assessment, the quick-response protocol, was tested on a pilot site on the Emilia-Romagna (Italy) coast in the aftermath of an extreme meteorological event that occurred in February 2015. Physical data were collected using both real-time kinematic Geographical Positions Systems and unmanned aerial vehicle platforms. Local stakeholders were interviewed by collecting qualitative information on their experiences before, during, and after the event. Data comparisons between local and regional surveys of this event highlighted higher data resolution and accuracy at the local level, enabling improved risk assessment for future events of this magnitude. The local survey methodology, although improvable from different technical aspects, can be readily integrated into regional surveys for improved data resolution and accuracy of storm impact assessments on the regional scale to better inform coastal risk managers during mitigation planning

Designing Improved Sediment Transport Visualizations

Monitoring, or more commonly, modeling of sediment transport in the coastal environment is a critical task with relevance to coastline stability, beach erosion, tracking environmental contaminants, and safety of navigation. Increased intensity and regularity of storms such as Superstorm Sandy heighten the importance of our understanding of sediment transport processes. A weakness of current modeling capabilities is the ability to easily visualize the result in an intuitive manner. Many of the available visualization software packages display only a single variable at once, usually as a two-dimensional, plan-view cross-section. With such limited display capabilities, sophisticated 3D models are undermined in both the interpretation of results and dissemination of information to the public. Here we explore a subset of existing modeling capabilities (specifically, modeling scour around man-made structures) and visualization solutions, examine their shortcomings and present a design for a 4D visualization for sediment transport studies that is based on perceptually-focused data visualization research and recent and ongoing developments in multivariate displays. Vector and scalar fields are co-displayed, yet kept independently identifiable utilizing human perception\u27s separation of color, texture, and motion. Bathymetry, sediment grain-size distribution, and forcing hydrodynamics are a subset of the variables investigated for simultaneous representation. Direct interaction with field data is tested to support rapid validation of sediment transport model results. Our goal is a tight integration of both simulated data and real world observations to support analysis and simulation of the impact of major sediment transport events such as hurricanes. We unite modeled results and field observations within a geodatabase designed as an application schema of the Arc Marine Data Model. Our real-world focus is on the Redbird Artificial Reef Site, roughly 18 nautical miles offshor- Delaware Bay, Delaware, where repeated surveys have identified active scour and bedform migration in 27 m water depth amongst the more than 900 deliberately sunken subway cars and vessels. Coincidently collected high-resolution multibeam bathymetry, backscatter, and side-scan sonar data from surface and autonomous underwater vehicle (AUV) systems along with complementary sub-bottom, grab sample, bottom imagery, and wave and current (via ADCP) datasets provide the basis for analysis. This site is particularly attractive due to overlap with the Delaware Bay Operational Forecast System (DBOFS), a model that provides historical and forecast oceanographic data that can be tested in hindcast against significant changes observed at the site during Superstorm Sandy and in predicting future changes through small-scale modeling around the individual reef objects