Boosting climate risk assessment with ML
Global warming is exacerbating weather, and extreme climatic events and is projected to aggravate risks across multiple sectors. Assessing and managing the multiple risks posed by interacting anthropogenic and natural drivers (including climate change) is one of the major challenges that the research community is currently facing.
This is particularly crucial for MCEs, where little is known about the complex inter-relationships between CC, biodiversity, and ecosystem services (ES) flow, due to limited data availability.
The complexity of MCEs, and their spatio-temporal dynamics causes major challenges when trying to understand cumulative risks in these systems. Challenges include identifying sites at high risk of cumulative effects (hotspots), and determining the relationships and synergies between multiple pressures that may interact to cause severe impacts.
What are the competitive benefits of ML, and how it may help to better analyse and manage current and future climate change risks on MCEs?
Thanks to the current digitization of EU and international society, ML-based methods can provide an alternative approach to characterizing complex environmental systems and provide reliable quantification of the effect of human activities on MCEs along with the interacting climate-driven and local/global anthropogenic factors affecting MCEs.
ML methods can be broadly applied across different environments, (e.g., river basins, lakes, mainland, coastal areas, and urban areas), and they can be used to carry out diverse tasks while taking into account various stressors, and hazards, exposure, and vulnerability factors (e.g., climatic, economic, social, demographic, cultural, institutional, governance and environmental).
ML for MaCoBioS
Within the MaCoBioS project, we exploit the potential of ML, as well as the huge amount of data that is available for environmental observation and monitoring (e.g. remote sensing data from Copernicus CMEMS and Sentinel missions, USGS Earth Explorer, among others). ML models will be used to assess the response of MCEs to the cumulative impacts caused by CC and human activities, including resulting changes to ES capacity and flow in these systems.
We will use scenario-analysis to assess the response of MCEs under multiple ‘what-if’ multi-risk scenarios, modelled by considering different CC conditions and management strategies. Furthermore, by using worst-case scenarios, ML models can explore the resilience and tipping points of marine coastal socio-ecological systems to multiple risks.