The ARCADIA Climate Risk Adaptation Toolbox is a harmonised collection of operational Climate Risk Assessment (CRA) workflows developed to assess the performance of Nature-based Solutions (NbS) and Blue-Green Infrastructure (BGI) by comparing a baseline configuration against an NbS/BGI scenario. The Toolbox is validated based on the first round of Co-innovation Labs.
What is the ARCADIA Toolbox?
The Toolbox is delivered as an online tool (a GitBook-based repository), which is the primary user-facing output. It organizes workflows into navigable pages, provides practical guidance on data, indicators, and decision-support tools, and clarifies prerequisites, with a focus on replicability and reuse.
A static report snapshot is provided by Deliverable 8.2, which complements the online resource by documenting the methodological backbone, the validation approach, and the evidence base from the Co-innovation Labs.
The ARCADIA Toolbox is a living resource, designed to evolve through further regional testing and refinements from continued Co-Innovation Lab work.
Key features
The Toolbox provides practical guidance on:
- data
- indicators
- decision-support tools
How to use it
The case studies are structured as replicable technical workflows. They demonstrate how to apply the Hazard–Exposure–Vulnerability logic to specific regional challenges (e.g., landslides, urban heat, flooding) and, crucially, how to quantify the performance of NbS by comparing a Baseline Scenario against an Adaptation Scenario. Each tutorial follows a harmonised 4-step methodology:
- Context & Objectives – Define the hazard, the affected sector, and the specific NbS/BGI intervention being tested.
- Step 1 – Data Acquisition: identify the minimum required datasets (local high-resolution vs. EU open data) and pre-processing steps.
- Step 2 – Model Setup: configure the specific tools, from GIS-based screening to physical models.
- Step 3 – Analysis: characterise the current risk and baseline indicators.
- Step 4 – NbS Scenario Testing: implement the NbS in the model and compare results using performance indicators.