Rising risk: Analyzing climate change's impact on Venetian coastlines

Image by Michelle Raponi from Pixabay

Coastal zones are dynamical and fragile environments at the boundary between land and water, shaped and constantly transformed by intricate interactions among various physical, ecological, and socio-economic factors.
These areas are particularly vulnerable to the impacts of climate change, facing threats from rising sea levels, extreme waves, and storm surges, impacting land, biodiversity, and ecosystems.

A study published in the journal Science of the Total Environment focuses on the Italian coastal city of Venice and the effects of climate change on the erosion of its coast and the quality of its water. The Venice lagoon represents an outstanding example of a semi-lacustral habitat which has become vulnerable because of irreversible natural, environmental and climate changes.

The paper “Multi-model chain for climate change scenario analysis to support coastal erosion and water quality risk management for the Metropolitan city of Venice” is the result of a study led within the project "Venezia2021" by a team of researchers of CMCC@Ca’Foscari, the strategic partnership between CMCC Foundation and Ca’ Foscari University of Venice.

This work represents the first attempt to integrate numerical hydrodynamic, wave, and coastal dynamic models to explore the impacts of both projected sea level rise and atmospheric circulation changes on regional coastal dynamics under climate change scenarios.
The joint team of researchers at CMCC and Ca’ Foscari University of Venice team coordinated the research activities, including the conceptual framework, the data collection, and the model development and implementation.

This research is based on a multi-model chain approach, combining ocean hydrodynamics, wave fields, and shoreline models to build a coastal risk assessment model for the future analysis of shoreline evolution and seawater quality.

“The innovation of the proposed multi-model chain centers on the capability of the Bayesian Network model to integrate heterogeneous data sources from different models, as well as the interactions among them,” said Hung Vuong Pham, researcher at CMCC Foundation and Ca’ Foscari University of Venice, and first author of the paper. “The cause–effect inference of the Bayesian model allows an effective and affordable computational scenario analysis while dealing with a large amount of high-resolution and detailed data.”

The results of the study highlight that the critical factor determining the increased frequency of extreme events in the Venice Lagoon is the change in mean sea level. Moreover, sea water velocity is the most influential factor on water quality parameters, while sea surface height and wind direction are the most dominant factors for shoreline change.

The study revealed concerning changes in shoreline and seawater quality under the RCP8.5 climate change scenario. After a stable period during the decade from 2021 to 2030, the shoreline is expected to experience erosion in the following decade (2031–2040) followed by accretion in the timeframe 2041–2050, coinciding with worsened seawater quality in terms of higher turbidity.

“The major advantage of the proposed multi-model chain lies in the integration of a regional model for waves, sea levels, and storm surges with a global climate model into a Bayesian Network Coastal Risk model that bridges the gap between the coarse resolution of global climate change projections and the detailed data required to investigate the impacts of climate change on regional coastal dynamics,” said Elisa Furlan of CMCC Foundation and Ca’ Foscari University of Venice, and author of the paper.
The combination of regional and global climate models with Machine Learning techniques and satellite imagery represents an innovative approach that offers a promising avenue for comprehending the diverse impacts anticipated under future climate change conditions, including factors like wind, waves, tides, and sea levels.
“The flexibility of the approach would represent a useful tool for forward-looking coastal risk management for decision-makers in designing integrated management plans for marine coastal areas,” said Pham.