Risk profiles for coastal and fluvial flooding were developed for Massa, Oarsoaldea, and Vilanova CCLLs using a methodology that integrates hazard, exposure, and vulnerability data. These assessments cover population, buildings, roads, and railways under various climate scenarios, including the presence or absence of Ecosystem-Based Approaches (EBAs). Based on this, an interactive Decision Support System (DSS) tool—developed in Python—enables users to visualise and compare layered financial strategies (e.g. reserve funds, insurance) through “as-if” cash flow simulations. This helps optimise costs and minimise residual losses.
The tools are designed for Municipal and Regional Public Bodies, particularly those responsible for climate adaptation, planning, civil protection, finance, and infrastructure. They support informed decision-making by illustrating both the scale of risk and the benefits of adaptation. While powerful and flexible, the Python-based DSS may require user training or technical support for effective implementation.
Addressing target audiences and expressing needs
- To raise awareness and possibly influence policy
- Expanding to more markets /finding new customers
The development of these tools revealed several key barriers to tool uptake, including low familiarity with financial risk methodologies, technical limitations due to Python-based accessibility, and the need for clearer, more visual presentation of results. Despite this, the tools are viewed as highly valuable for policy making and strategic planning. Improvements to the DSS tool would be useful to increase its usability, with a more web-accessible interface, to enhance usability.
- EU and Member State Policy-makers
- Other Actors who can help us fulfil our market potential
R&D, Technology and Innovation aspects
Massa, Oarsoaldea, and Vilanova i la Geltrú CCLLs conducted flood risk assessments and co-developed financial strategies and a DSS tool. Using local data and EBAs, they evaluated risks to people and infrastructure. Tools were tested in KT sessions; feedback improved usability. Stakeholders found them useful but noted the need for clearer visuals and support materials.
The scalability of the SCORE Risk Profiles and DSS stems from their modular design and use of open data. The methodology can be adapted to new coastal cities by adjusting local hazard, exposure, and socio-economic inputs. The scenario-based DSS supports expansion to more locations and risks, and its Python-based architecture allows for upgrades or integration with platforms like Copernicus. With investment, it can evolve into a web-based tool. Capacity building, training, and strong stakeholder partnerships are key to ensuring broad adoption and long-term impact across Europe.
The SCORE Risk Profiles and Decision Support System (DSS) are highly replicable thanks to their transparent methodology, use of openly available data, and standardised indicators. By combining hazard, exposure, and vulnerability data into semi-quantitative indices, the approach allows consistent application across different cities. The tool is designed for adaptation by updating local input datasets without changing the core structure. It’s accessible to both technical users and policymakers, making it broadly usable. Developed in Python with standard libraries, the DSS can be modified or extended, and is supported by clear documentation and use cases. Knowledge Transfer sessions have already demonstrated successful replication in new cities. These features ensure it can be applied widely across coastal regions in Europe and beyond.
The result and proposed business model are sustainable long-term.
