EGU26-5249, updated on 13 Mar 2026
https://doi.org/10.5194/egusphere-egu26-5249
EGU General Assembly 2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Poster | Friday, 08 May, 14:00–15:45 (CEST), Display time Friday, 08 May, 14:00–18:00
 
Hall X3, X3.39
Improving Sea Level Height warnings in Venice (Italy) and Alexandria (Egypt) with hybrid sub-seasonal forecasts
Antonello Squintu1, Mehri Hashemi Devin1, Angela Andrigo2, Alessandro Tosoni2, Eman Shaker3, Elena Xoplaki1, Alvise Papa2, and Enrico Scoccimarro1
Antonello Squintu et al.
  • 1CMCC Foundation - Euro-Mediterranean Centre on Climate Change, Bologna, Italy (antonello.squintu@cmcc.it)
  • 2CPSM - Center for the Forecast and Warning of Tides, Venice Municipality, Venice, Italy
  • 3EMA - Egyptian Meteorological Authority, Cairo, Egypt

The city of Venice (Italy) is highly vulnerable to weather-driven Sea Level Height (SLH) surge, which causes serious disruptions to city services (e.g. water-ambulances and water-buses) and damage to commercial and cultural assets. Similarly, due to its orography, Alexandria (Egypt) suffers from coastal floods, which heavily affect infrastructure. Early detection of these events is of paramount importance to increase the preparedness of citizens and stakeholders and to optimize the organization of major events. The increased frequency and intensity of High Water events are linked to the rise in average global SLH and to  the combination of astronomical tide and weather-driven SLH surge. While the first two components can be accurately determined via observations and astronomical calculations, the meteorological contribution requires weather forecasts as inputs. The MedEWSa project aims to improve the Early Warning Systems (EWS) of the two case studies by enhancing the forecasts of weather-driven SLH anomalies employing AI algorithms. This work began with the use of the evolutionary algorithm PCRO-SL (Probabilistic Coral Reef with Substrate Layers) on ERA5 reanalysis data to detect, among a set of candidates in the Euro-Mediterranean domain, the relevant lagged drivers of SLH anomaly. These drivers were used to train multiple Neural Networks and Tree-Based models, with in-situ observations as target series. The algorithms were fine-tuned and evaluated with the objective of identifying the most suitable one. The selected model has been implemented for daily application to the latest issued forecasts, providing the Venice Municipality Control Room with predictions of SLH extended to the sub-seasonal time horizon. These forecasts are currently being compared with the output of the standing system, assessing the added value and the improved capability of the EWS. Concurrently, the experience gained from the Venetian case has been transferred to the Egyptian case, allowing the initialization of a SLH EWS and increasing the preparedness of the city of Alexandria to coastal floods.

How to cite: Squintu, A., Hashemi Devin, M., Andrigo, A., Tosoni, A., Shaker, E., Xoplaki, E., Papa, A., and Scoccimarro, E.: Improving Sea Level Height warnings in Venice (Italy) and Alexandria (Egypt) with hybrid sub-seasonal forecasts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5249, https://doi.org/10.5194/egusphere-egu26-5249, 2026.