FOCAL Urban Pilot: Efficient exploration of climate data locally for data-driven decision-support in urban climate adaptation planning

Climate change impacts are increasingly manifested at local scales, where mitigation and adaptation strategies are implemented. Despite the growing wealth of available climate data and services, their effective usage in local climate impact assessment and decision-making processes for mitigation and adaptation planning remains limited due to scale mismatches, computational constraints, complexity, and usability barriers for non-domain experts. Addressing these challenges requires both advanced computational methods and improved access to climate data and analysis tools.

 

The EU Horizon project, FOCAL, bridges the gap between data, services, and their users by implementing an open compute platform that combines intelligent workflow management with high-performance computing (HPC) resources to allow for an efficient exploration of climate data on a local scale. In addition, innovative artificial intelligence (AI) tools are developed and made available to enhance climate data analysis in terms of speed, robustness, pattern detection, and localization; thereby expanding the toolkit of climate data analysis and impact assessment methods.

 

A main objective of FOCAL is to support science-based, actionable decision-making processes in forestry and urban planning through its provided tools. In a co-design process involving developers and potential platform users from two forest pilot regions with contrasting ecological and management contexts (Forest Pilots) as well as a pilot city (Urban Pilot), web applications for intuitive user-platform-interaction and workflows, grounded in state-of-the-art climate science, to address concrete user questions in forestry and urban planning have been specified. As a result, decision makers can efficiently use climate data for the development of climate adaptation strategies.

This contribution focuses on the Urban Pilot, implemented for the pilot city Constance (Baden-Württemberg, southern Germany), located at the western end of Lake Constance. Three core workflows have been developed:

1) Regional climate change workflow: provision of robust regional climate change information for the past and the future under different global warming levels for urban areas, based on regional climate model and localized climate data, serving multi-sectoral local climate impact assessments;

2) Urban hot and cool spot workflow: detection and high-spatial-resolution visual exploration of hot and cool spots in urban environments, supporting exposure assessment by integrating additional data (e.g., population or infrastructure data), risk assessment, and the planning of urban heat resilience measures and cooling spaces;

3) Urban blue spot workflow: identification of blue spots (rainfall accumulation hazards) and provision of blue spot data in urban landscapes using processed precipitation data and extreme precipitation scenarios, supporting applications in hydrological modeling, flood risk management, and climate adaptation.

By leveraging HPC-based data processing and AI-assisted analysis, these workflows translate complex climate data into actionable, locally relevant information. While demonstrated for the pilot city Constance, the methods and workflows are transferable to other urban areas, contributing to scalable and reproducible climate services.