EGU23-8702, updated on 26 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Evaluating the risk of cumulative impacts in the Mediterranean Sea using a Random Forest model

Angelica Bianconi1,2,3, Elisa Furlan2,3, Christian Simeoni2,3, Vuong Pham2,3, Sebastiano Vascon2,4, Andrea Critto2,3, and Antonio Marcomini2,3
Angelica Bianconi et al.
  • 1Department of Science, Technology and Society, Scuola Universitaria Superiore Pavia (IUSS), Pavia, Italy
  • 2Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, I-30170 Venice, Italy
  • 3Fondazione Centro-Euro-Mediterraneo sui Cambiamenti Climatici, I-73100 Lecce, Italy
  • 4European Center for Living Technology, Ca’ Foscari University of Venice, Venice, Italy

Marine coastal ecosystems (MCEs) are of vital importance for human health and well-being. However, their ecological condition is increasingly threatened by multiple risks induced by the complex interplay between endogenic (e.g. coastal development, shipping traffic) and exogenic (e.g. changes in sea surface temperature, waves, sea level, etc.) pressures. Assessing cumulative impacts resulting from this dynamic interplay is a major challenge to achieve Sustainable Development Goals and biodiversity targets, as well as to drive ecosystem-based management in marine coastal areas. To this aim, a Machine Learning model (i.e. Random Forest - RF), integrating heterogenous data on multiple pressures and ecosystems’ health and biodiversity, was developed to support the evaluation of risk scenarios affecting seagrasses condition and their services capacity within the Mediterranean Sea. The RF model was trained, validated and tested by exploiting data collected from different open-source data platforms (e.g. Copernicus Services) for the baseline 2017. Moreover, based on the designed RF model, future scenario analysis was performed by integrating projections from climate numerical models for sea surface temperature and salinity under the 2050 and 2100 timeframes. Particularly, under the baseline scenario, the model performance achieved an overall accuracy of about 82%. Overall, the results of the analysis showed that the ecological condition and services capacity of seagrass meadows (i.e. spatial distribution, Shannon index, carbon sequestration) are mainly threatened by human-related pressures linked to coastal development (e.g. distance from main urban centres), as well as to changes in nutrient concentration and sea surface temperature. This result also emerged from the scenario analysis, highlighting a decrease in seagrass coverage and related services capacity, in both 2050 and 2100 timeframes. The developed model provides useful predictive insight on possible future ecosystem conditions in response to multiple pressures, supporting marine managers and planners towards more effective ecosystem-based adaptation and management measures in MCEs.

How to cite: Bianconi, A., Furlan, E., Simeoni, C., Pham, V., Vascon, S., Critto, A., and Marcomini, A.: Evaluating the risk of cumulative impacts in the Mediterranean Sea using a Random Forest model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8702,, 2023.