Sergio Castellari

Dr. Sergio Castellari

Istituto Nazionale di Geofisica e Vulcanologia (INGV)—Sezione di Bologna, 40128 Bologna, Italy; European Environment Agency (EEA), Copenhagen, Denmark

Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 December 2019 in Climate
Verified
Journal Article
Open access
PDF file
GVGiulia Villani
Giulia Villani
SNStefania Nanni
Stefania Nanni
FTFausto Tomei
Fausto Tomei
SPStefania Pasetti
Stefania Pasetti
RMRita Mangiaracina
Rita Mangiaracina
SCSergio Castellari
Sergio Castellari
Many urban areas face an increasing flood risk, which includes the risk of flash floods. Increasing extreme precipitation events will likely lead to greater human and economic losses unless reliable and efficient early warning systems (EWS) along with other adaptation actions are put in place in urban areas. The challenge is in the integration and analysis in time and space of the environmental, meteorological, and territorial data from multiple sources needed to build up EWS able to provide efficient contribution to increase the resilience of vulnerable and exposed urban communities to flooding. Efficient EWS contribute to the preparedness phase of the disaster cycle but could also be relevant in the planning of the emergency phase. The RainBO Life project addressed this matter, focusing on the improvement of knowledge, methods, and tools for the monitoring and forecast of extreme precipitation events and the assessment of the associated flood risk for small and medium watercourses in urban areas. To put this into practice, RainBO developed a webGIS platform, which contributes to the “planning” of the management of river flood events through the use of detailed data and flood risk/vulnerability maps, and the “event management” with real-time monitoring/forecast of the events through the collection of observed data from real sensors, estimated/forecasted data from hydrologic models as well as qualitative data collected through a crowdsourcing app.