“I need information of the development of exposed population in the future for different combinations of climate and socioeconomic scenarios on a regional to national scale, so I can preliminary assess exposure to coastal flooding.”
1. What is a CoCliCo User Story?
User Stories are ready-made map datasets in the CoCliCo platform. They combine different types of important information to show scenarios for coastal risk resulting from sea-level rise, floods and / or erosion. These layers make complex analyses easier and help users to quickly get a sense of coastal risks.
User research showed that policymakers need clear, actionable data for flood directives, while urban planners want tools to assess local risks, and where infrastructure managers focus on long-term resilience planning. These insights helped shape User Stories to provide accessible, scenario-driven visualizations for diverse decision-making needs. There are six User Stories:
- Sea Level Rise Projections
- Inundation Distribution During a Flood Event
- Building Exposure
- Projections of Exposed People
- Damage Costs of Exposed Infrastructures
- Adaptation Based on Cost-Benefit Analysis
In this e-guideline, we walk you through the Projections of Exposed People User Story.
2. Introduction to the Projections of Exposed People User Story
The Projections of Exposed People User Story in the CoCliCo platform shows how many people may be affected by coastal flooding in the future. It combines high-resolution flood and projected population data to provide clear insights under different climate and socioeconomic scenarios.
Since future population growth and movement are uncertain, this tool considers multiple scenarios of population development to improve flood risk assessments. By mapping projected population exposure to coastal flooding at a high spatial scale, it helps policymakers, urban planners, and resilience experts make informed decisions for adaptation and risk reduction.
3. Step-by-Step Platform Usage
- Access the CoCliCo Platform:
- On the left-hand menu bar, navigate to the “Exposure & Vulnerability” category, then “Projections of Exposed People” under “User Stories”.
- Select a Scenario:
- Choose a climate scenario (e.g., SSP1-2.6, SSP2-4.5, etc.), the percentile of the probabilistic projection (e.g. 17th, median or 83rd) and time horizon.
- Analyze Visualizations:
- Use interactive tools to zoom into regions and access localized insights.
- Use the “Add to Dashboard” feature to retain charts and graphs for further comparison and analysis
- Toggle on other layers, such as “natural hazards” and “exposure & vulnerability,” for more context on the impacts and risks faced by that area. For simpler comparisons, make sure you add your charts and graphs to your dashboard to compare across time and geographies and observe various layers and user stories of that area.
- Further Analysis:
- Export maps or raw datasets for further analysis in the Workbench or other GIS tools.
- Assess local projections with vertical land motion (VLM) using our coastal hazard assessment.
4. Target Users & Intended Use
Target Users:
- Policymakers implementing regional, national and EU flood directives.
- Urban planners and city authorities in coastal areas.
- Researchers and consultants assessing coastal population exposure.
Intended Use:
The layer provides simplified, robust visualizations for identifying areas of high population exposure to coastal flooding. It supports broad scale, as well as localised exposure assessments to enable preliminary risk assessments and adaptation planning.
Key Benefits:
- Enables informed exposure assessments and adaptation planning at all scales (national to municipal).
- Improves stakeholder engagement with clear, accessible data.
- Aids investment prioritization and strategic land-use decisions.
5. Example of Use
“Using the exposed population projections User Story, local authorities identified a concentration of population exposure in a specific area by 2050 under a high sea level scenario and low defence level. This analysis initiated a systematic and targeted adaptation planning process in this area, focusing on enhancing defences to eliminate the population exposure.”
6. Data, Methods, and Model Overview
Data Sources:
CoCliCo’s population projections use IIASA data, downscaled with historical trends and spatial factors like roads, urban areas, and coastline proximity. Areas unsuitable for development, such as steep slopes, water-covered regions, and protected areas, are excluded. These projections are combined with CoCliCo flood data to assess population exposure to coastal flooding.
- Based on national population and urbanization projections from IIASA
- Downscaled and spatially distributed using historic population data (GHS-POP)
Projections account for key geographic and infrastructural influences. Distance to:
- Roads (OSM data)
- Urban areas (defined by population density, Degree of Urbanization method)
- Coastline (MERIT DEM coastal mask)
- Urban centres (travel time model by Weiss et al., 2018)
Excluded from future population growth
- High elevation or steep slopes (MERIT DEM)
- Permanently flooded areas (Corine Land Cover)
- Protected areas (World Database on Protected Areas)
Methods:
CoCliCo’s population projections are created by extending the model of Reimann et al. (2021) that helps distribute updated national population data from IIASA (2010–2100) in 10-year intervals. The data is broken down at a 1 km resolution for EU countries and the UK, for different Shared Socioeconomic Pathways (SSPs).
The model works by first calculating the “potential” or attractiveness of each area (or grid cell). It then distributes national population changes over time based on this potential. The “potential” of an area is influenced by factors like how close it is to nearby areas, population density, and distance from the coastline. The model also considers past patterns of people moving between coastal/inland and urban/rural areas, adjusting for two time periods. In addition, urbanization projections are used to account for the different ways urban and rural areas develop over time in each SSP.
These population projections are combined with flood projections based on the integrated climate and socioeconomic scenarios to assess how many people are exposed to coastal flooding. The population and flood projections are aligned to ensure they match in both projection and resolution, allowing for accurate calculation of exposed populations at the Local Administrative Units (LAUs) level in coastal areas. These results are then summarized at smaller spatial levels.
Model Outputs:
- Visualizations display gridded population projections for the years 2010, 2030, 2050 and 2100 for five integrated scenarios (No SLR-SSP2, SSP1-2.6, SSP2-4.5, SSP5-8.5 and one high-end scenario with SSP5).
- Interactive features allow scenario comparisons and data downloads for local to national decision-making.
Limitations of Population Projections
- Population Decline: In areas with population decline, the model treats cell potential differently. In urban areas, higher cell potential is linked to population loss due to suburbanization. In rural areas, lower cell potential is associated with higher population loss. This is a general assumption and might not apply to all EU regions.
- Urban and Rural Definitions: Urban and rural areas are redefined after each timestep based on urbanization share. However, this measure only reflects the fraction of the population living in urban areas and doesn’t capture the complex structure of urban regions.
Limitations of Exposed Population
- Data Alignment: To combine flood and population projections, both need to have the same resolution and projection. We adjust the population data to match the flood projections, but this can affect population counts.
- Coastline Differences: The population and flood projections use different coastlines, so some people living near the coast might not be considered exposed, even if they are on the ocean side of the coastline.
7. Further Analysis
To account for the full range of uncertainty in population development and its impact on coastal flooding, it’s useful to explore other socioeconomic scenarios beyond the integrated ones. While these additional estimates aren’t included directly in the platform, they can be explored in the workbench by combining various climate and socioeconomic scenarios at different spatial scales.
