This study illustrates the implementation of a local-scale post-event survey that combined GPS and UAV-based techniques with qualitative information collected through interviews with local stakeholders. The comprehensive approach employed in this case study was conducted on the Emilia-Romagna coast (Italy), in the immediate aftermath of an extreme event that impacted the shoreline on the 5-6 February 2015, called the St Agatha storm.

To enable efficient coastal management, present and future scenarios must be included, risk reduction measures integrated, and multiple hazards dealt with. Process-based models are used to predict hazards at receptors. Impacts are calculated through vulnerability relations. Simulations are integrated with a Bayesian-based approach to link source with consequences. The tool is valuable for communicating risks and the effects of risk reduction strategies and as support for coastal decision making.

The performance of a state-of-the-art early-warning system for the coastline of Emilia-Romagna in northern Italy is rigorously assessed with regards to a major storm event that occurred in October 2012. It is found that such a system has great potential as a new tool for coastal management, following several improvements to the forecast model chain. What-if scenarios in terms of the construction of artificial dunes prior to this event suggest that this may have helped minimize storm impacts.

The paper compares two methodologies adopted by the Emilia-Romagna region, northern Italy, to evaluate coastal vulnerability and to produce hazard and risk maps for coastal floods, in the framework of the EU Floods Directive. The flooded area extension is determined by a series of computations, whose core is the Cost Distance tool of ArcGIS^®. The qualitative validation and the comparison between the two methods show a positive agreement.

Impacts of storm event chronology in a storm cluster was investigated. The largest event-driven bed level change occurred under the most powerful storm event when it initialised the cluster, and the lowest bed level change occurred for the weakest event when it ended the cluster. Negligible variability in the cumulative impact of the storm clusters occurred in response to different storm wave chronologies. However, the highest erosion was found when the storms approached in increasing severity.

A pressing problem facing coastal decision makers is the conversion of "high-level" but plausible climate change assessments into an effective basis for climate change adaptation at the local scale. Here, we describe a web-based, geospatial decision support tool (DST) that provides an assessment of the potential flood risk for populated coastal lowlands arising from future sea-level rise, coastal storms, and high river flows.