Key Layers, Sources, and How to Interpret Them

Data Layers in the CoCliCo platform test

In the CoCliCo Platform, each data layer is the result of modeling and transforming various datasets from the STAC (SpatioTemporal Asset Catalog) to generate the final geospatial data layers. The platform is organized into five main categories: Sea Levels, Natural Hazards, Exposure and Vulnerability, Risk and Adaptation, and Background Layers, each containing its own specific data layers. Keep reading to discover which datasets are used to create the data layers in the platform.

User Stories

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 five User Stories:

Flood Perspectives
People Exposure
Building Exposure
Cost Benefit Analysis
Damage Costs

Sea Levels

Data Layers & User Stories

Sea Level Rise ProjectionsFuture Total Water Levels and Return PeriodsDrivers of Total Water Level

The CoCliCo Platform provides access to sea-level rise (SLR) projections based on the latest scientific assessments from the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), essential for understanding future changes and planning coastal adaptation.

Use the Sea Level Rise Projections User Story for detailed insights into how sea levels may change under different climate scenarios. With projections ranging from 0.3 to 1 meter by 2100—and continuing to rise—this data is crucial for assessing coastal flood risks, infrastructure planning, and long-term adaptation.

Unlike global estimates, these regional projections account for local factors like ocean circulation, ice melt, and land shifts, offering more precise insights for specific locations. This layer is a key foundation for flood models and supports all other User Stories in the platform.

"I need to see mean sea-level rise information now and in the future for different climate change scenarios, so I can do a broad-scale preliminary evaluation of risks.”

Data Sources

CoCliCo's regional sea-level projections are based on the IPCC AR6 dataset, incorporating all sea-level components except vertical land motions, which are corrected using GIA model outputs for improved regional accuracy.

IPCC AR6 (Fox-Kemper et al., 2021)
AR6 dataset is described and displayed at Sea Level Projection Tool – NASA Sea Level Change Portal and is publicly distributed at IPCC AR6 Sea Level Projections (Garner et al., 2022).
Glacial Isostatic Adjustment (GIA) model outputs (Caron et al., 2018)

Methods

CoCliCo regional mean sea-level projections are constructed by combining the IPCC AR6 sea-level change dataset and the GIA model outputs of Caron et al. (2018), and propagating uncertainty following a Monte Carlo approach. The regional sea-level changes therefore include the changes in ocean density and circulation, the changes due to continental glaciers and ice-sheet mass loss and their respective regional spatial distribution, changes in land water and groundwater, and the post-glacial rebound.

Climate Scenarios

The platform offers sea-level rise projections for three Shared Socioeconomic Pathways (SSPs) and a high-end scenario:

SSP1-2.6
A low-emission scenario where global temperatures are limited to 1.5°C above pre-industrial levels, reflecting a sustainable future with rapid decarbonization.
SSP2-4.5
A medium-emission scenario where global temperatures rise moderately, reflecting a future with some efforts to mitigate climate change.
SSP5-8.5
A high-emission scenario where global temperatures rise significantly, reflecting a future with continued high greenhouse gas emissions and limited mitigation efforts.
High-End
Represents more extreme but plausible outcomes of sea-level rise, useful for risk assessment and worst-case planning.

Ensembles

The projections are provided in three ensemble ranges to reflect the uncertainty in future sea-level rise:

MSL_h: Represents the upper range of projected sea-level rise, reflecting higher uncertainty and more extreme outcomes.
MSL_m: Represents the median projection of sea-level rise, based on the central estimates from the IPCC AR6.
MSL_l: Represents the lower range of projected sea-level rise, reflecting more optimistic outcomes with lower uncertainty.

Time Horizon

The projections are available for decadal time steps from 2030 to 2150, allowing users to explore how sea levels may change over the coming decades and into the next century.

Regional and Global Coverage

The projections are provided at both regional and global scales:

Regional Scale: Users can explore how sea-level rise may vary across different parts of the world, accounting for local factors such as land subsidence, ocean currents, and glacial isostatic adjustment.
Global Scale: The platform also provides global mean sea-level (GMSL) projections, which represent the average rise in sea levels worldwide.

Baseline Period

All projections are relative to a 1995–2014 baseline period, consistent with the IPCC AR6 methodology. This baseline provides a common reference point for comparing future sea-level rise scenarios.

How to Use the Sea Level Rise Projections

Scenario Selection: Choose from the available scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5, and high-end) to explore different future pathways of sea-level rise.
Ensemble Selection: Select between the high (MSL_h), median (MSL_m), and low (MSL_l) ensembles to understand the range of possible outcomes.
Time Horizon: View projections for specific decades (e.g., 2030, 2040, 2050, etc.) to assess how sea levels may change over time.
Specific Analysis: Zoom in on specific regions to see how sea-level rise may impact local coastlines.

Model Outputs

Why Are These Projections Important?

Coastal Risk Assessment: The sea-level rise projections are critical for assessing the risks of coastal flooding, erosion, and other hazards. They help identify areas that may be most vulnerable to future sea-level rise.
Adaptation Planning: By understanding how sea levels may change in the future, coastal planners and policymakers can develop strategies to protect communities, infrastructure, and ecosystems.
Scientific Consistency: The projections are based on the latest IPCC AR6 report, ensuring that users have access to the most reliable and up-to-date scientific information.

Example of use

"Using the Sea Level Rise projections User Stories, city planners identified their neighbourhoods as having a higher risk of permanent flooding related to sea level rise by 2050 under high-emission scenarios high-emission scenarios compared to other neighbourhoods in the country. This analysis informed their decisions to prioritize green infrastructure development in those areas, reducing potential damage costs by 30%."

Limitations

Limitations are twofold: first, vertical ground motions unrelated to the glacial isostatic adjustment are not integrated. Yet CoCliCo’s research has shown that urban areas and populations located in coastal flood plains in Europe (excluding Fennoscandia) are affected by subsidence of approximately 1mm/year in average.

Second, sea level projections shown here have a resolution of 1°x1°, therefore not taking into account mesoscale ocean processes acting on the continental shelf and within semi-enclosed bassins such as the Mediterranean. Research undertaken by CoCliCo by ENEA and Mercator Ocean (D3.3, soon to be published) suggest that the order of magnitude of the error due to neglecting these processes can reach +/-10cm in Europe.

Further Analysis

Beside GIA, coastal regions in Europe can experience significant vertical land motion (VLM) which can be strong, robust and that can be assessed locally. There is for instance well known subsidence along the Italian Adriatic, the Netherlands or even in more localized shorelines such as the Aksiou delta next to Thessaloniki in Greece. This subsidence context can strongly inflate coastal hazards locally and should therefore be accounted for. The CoCliCo project explored local VLMs using the land vertical velocity estimates from the Copernicus European Ground Motion Service (EGMS) derived over the period 2016-2021 (Thiéblemont et al., 2024). While these estimates are not implemented in the regional sea-level projections of CoCliCo, they have been considered for the coastal hazard assessment and can be explored as an exploratory tool using the Workbench.

The Extreme Surge Level (SSL) data layer provides projections of storm surge levels along the European coastline under different climate scenarios. Storm surges are a critical component of coastal hazards, and understanding how they may evolve in the future is essential for coastal risk assessment and adaptation planning. This dataset is part of the LISCOAST project and is based on hydrodynamic modeling and climate projections.

"Example"

Data Sources

Methods

The SSL projections are based on:

Hydrodynamic Modeling

The Delft3D-Flow model was used to simulate storm surge dynamics.
Climate Forcing

The model was forced by surface wind and atmospheric pressure fields from an 8-member climate model ensemble.
Validation

The model was validated using data from 110 tidal gauge stations across Europe, showing good predictive skill (RMSE: 0.06 m to 0.29 m).
Extreme Value Analysis

The Peak-Over-Threshold (POT) method was applied to estimate SSL values for different return periods.

Climate Scenarios

The dataset includes projections for three climate scenarios:

Historical
Represents the baseline period (1970–2000) for validating the model.
RCP4.5
A medium-emission scenario where global temperatures rise moderately.
RCP8.5
A high-emission scenario where global temperatures rise significantly.

Return Periods

The dataset provides SSL estimates for eight return periods, which represent the frequency of extreme events:

Return Period (Years)	Description
5	Events expected to occur once every 5 years.
10	Events expected to occur once every 10 years.
20	Events expected to occur once every 20 years.
50	Events expected to occur once every 50 years.
100	Events expected to occur once every 100 years.
200	Events expected to occur once every 200 years.
500	Events expected to occur once every 500 years.
1000	Events expected to occur once every 1000 years.

Geographic Coverage The dataset covers the European coastline, with detailed projections for:

Northern Europe
Areas north of 50°N, where significant increases in SSL are projected, especially under RCP8.5.
Southern Europe
Areas south of 50°N, where minimal changes or small decreases in SSL are projected, except under RCP8.5 towards the end of the century.

How to Use the Extreme Surge Level Data

Scenario Selection: Choose from the available scenarios (Historical, RCP4.5, RCP8.5) to explore how storm surge levels may change under different climate futures.
Return Period Selection: Select a return period (e.g., 5, 10, 20, 50, 100, 200, 500, or 1000 years) to assess the frequency and magnitude of extreme storm surge events.
Geographic Analysis: Zoom in on specific regions (e.g., Northern or Southern Europe) to see how storm surge levels may vary across the coastline.

Model Outputs

Why Is This Data Important?

Coastal Risk Assessment: The SSL data is critical for assessing the risks of coastal flooding and erosion, especially in areas prone to extreme storm surges.
Adaptation Planning: By understanding how storm surge levels may change in the future, coastal planners and policymakers can develop strategies to protect communities, infrastructure, and ecosystems.
Combined Effects: The dataset highlights the combined effects of relative sea-level rise (RSLR) and storm surge levels, which can significantly increase coastal hazards, particularly under high-emission scenarios like RCP8.5.

Example of use

"Example"

Further Analysis

The Extreme Sea Level (ESL) data layer provides projections of total water levels (TWL) along European coastlines under climate change. This dataset combines mean sea level rise, tides, storm surges, and wave heights to assess future coastal flood risks. It is part of the LISCOAST project and is critical for understanding how climate change will impact coastal hazards.

"Example"

Data Sources

Methods

Climate Scenarios The dataset includes projections for two climate scenarios:

RCP4.5
A medium-emission scenario where global temperatures rise moderately.
RCP8.5
A high-emission scenario where global temperatures rise significantly.

Return Periods The dataset estimates extreme sea levels for eight return periods, representing the frequency of extreme events:

Return Period (Years)	Description
5	Events expected once every 5 years.
10	Events expected once every 10 years.
20	Events expected once every 20 years.
50	Events expected once every 50 years.
100	Events expected once every 100 years.
200	Events expected once every 200 years.
500	Events expected once every 500 years.
1000	Events expected once every 1000 years.

Components of Extreme Sea Levels

Total Water Level (TWL)
Combines mean sea level (MSL), tides, storm surges, and wave setup.
Episodic Extreme Water Level (EEWL)
Includes extreme storm surge levels and wave heights during episodic events (e.g., storms).

Geographic Coverage The dataset covers Europe’s coastline, with regional projections highlighting:

North Sea: Highest projected increase in ESLs (up to 1 m under RCP8.5 by 2100).
Baltic Sea & UK/Ireland Atlantic Coasts: Significant increases in ESLs.
Southern Europe: Stable or decreasing extremes, except for Portugal and the Gulf of Cadiz.

How to Use the Extreme Sea Level Data

Scenario Selection: Choose between RCP4.5 (moderate warming) and RCP8.5 (high warming) to explore future risks.
Return Period Selection: Select a return period (e.g., 100-year event) to assess the magnitude and frequency of extreme sea levels.
Regional Analysis: Focus on specific regions (e.g., North Sea, Southern Europe) to understand local impacts.

Model Outputs

Why Is This Data Important?

Coastal Flood Risk: ESL projections help identify areas most vulnerable to flooding, particularly under high-emission scenarios.
Adaptation Planning: Supports decision-making for coastal defenses, land-use planning, and emergency preparedness.
Combined Effects: Highlights how relative sea level rise (RSLR), storm surges, and waves interact to amplify risks.

Example of use

"Example"

Further Analysis

Natural Hazards

Data Layers & User Stories

Flood Maps

The Flood Maps Data Layer provides clear, reliable visualizations of flood maps for identifying coastal areas at risk from flooding due to extreme weather events and sea-level rise. Besides risk identification and planning, the spatial distribution of coastal flooding projections is key to guide well-informed decisions and enhance community and stakeholder preparedness.

> *"Example"*

> *“I need to see maps of flood extent and depth for different sea-level rise and storm scenarios so I can assess coastal flood risks and identify vulnerable areas across Europe.”*

**Data Sources**

Topographic data used to represent the European floodplain terrain included a 25-m resolution digital elevation model (DEM) (Copernicus, 2019), the corresponding coastline used to define boundary conditions (EEA, 2017), and land-use information (Witjes et al., 2022), which was translated into Manning's roughness coefficients.

Marine dynamic forcing conditions were obtained using two approaches for 1 km-spaced coastal points along the European coastline. For permanent inundation scenarios, input data were derived from the CoCliCo Regional Sea-Level Rise (SLR) Projections. For episodic flood events, input data were obtained from extreme total water level (TWL) return level scenarios based on a reconstructed TWL hindcast. This hindcast consisted of the astronomical tide from the latest version of the TPXO database, storm surge simulated using the ROMS model (Shchepetkin & McWilliams, 2005), and wave setup estimated from wave conditions in a downscaled wave hindcast generated with the WaveWatch III model (Tolman, 2009).

Additionally, the dataset on protection standards around Europe’s coast developed by Vrije Universiteit Amsterdam was incorporated into the flood maps in a post-processing step (van Maanen et al., 2024).

## **Methods**

The methodology followed can be divided into three steps:
# European Coastal Flood Modelling Process

### (1) Definition of Floodplain and Meshes
- The European floodplain was defined as **coastal regions located between 0 and 15 m elevation** that are hydraulically connected to the sea.  
- This floodplain was segmented into **22 flood units**, from which **topographic meshes** were generated.  
- Each mesh is composed of **irregular cells (impact zones)** containing **sub-element topography** (impact cells with 25 m resolution inherited from the DEM).  
- Impact zones:
- Are flexible, following terrain features.  
- Boundaries adjust to topographic crests.  
- Each impact zone was assigned a **Manning roughness coefficient**, based on the majority land use present.

---

### (2) Hydrograph Construction

**Permanent inundation scenarios**
- Hydrographs were created by combining **sea-level rise (SLR)** with the **mean spring high tide** at each coastal point.

**Episodic flooding scenarios**
- Hydrographs were based on:
- **TWL (Total Water Level) extreme value analysis**.  
- A **storm duration function** for TWL storms.  
- TWL hindcast reconstructed by summing:
- Astronomical tide  
- Storm surge  
- Wave setup  
- Wave setup:
- Computed using the semi-empirical formulation of **Stockdon et al. (2006)**.  
- Foreshore slopes estimated via **Sunamura (1984)**.

**Extreme event detection**
- **Peak Over Threshold (POT) method** applied to identify TWL extreme events.  
- Threshold chosen to yield ~**2 events per year**.  
- **Return values** of TWL estimated by fitting extremes to an **exponential model**.  
- **Storm durations** for return-period events estimated from individual POT events.

**Combined scenarios**
- Future episodic flooding scenarios were obtained by **superimposing relative SLR** onto hydrographs generated from TWL return values (hindcast period).

---

### (3) Coastal Flood Simulations

- Simulations conducted with the **RFSM-EDA 2D flood model**.  
- RFSM-EDA:
- Efficient hydraulic model for **large-scale, process-based flood modelling**.  
- Incorporates topography as a **sub-element of the computational mesh**.  
- References: *Sayers and Marti (2006); Jamieson et al. (2012)*.  
- During simulations:
- **Saint-Venant equations** solved between impact zones.  
- Flood depth computed at each impact cell.  
- Output:
- Flood maps showing **depth and extent** under different scenarios across the European coastline.  
- Post-processing:
- Maps adjusted to reflect the effect of **coastal defences**.  
- Considered both **minimum and maximum policy-based return periods** at the **NUTS 2 (province) level**.

---

**Defence Level**

Process-based flood maps were post-processed to incorporate the effect of existing coastal defences. The dataset of protection standards used (van Maanen et al., 2024) provides estimates of the minimum and maximum policy-based return periods at the province level (NUTS 2). For each scenario, three flood maps were provided: one undefended map (without defences) and two defended maps (with defences). Defended maps represent the low- or high-defended cases depending on whether the minimum or maximum level of protection is assumed in each province.

| Defence Level  | Description                                                                 |
|----------------|-----------------------------------------------------------------------------|
| Undefended     | Without protection (beyond what may be included in the DEM).                |
| Low defended   | Maximum level of policy-based protection at the NUTS2 level                 |
| High defended  | Minimum level of policy-based protection at the NUTS2 level                 |

**Climate Scenarios**

The dataset includes projections for three climate scenarios with different levels of confidence of sea-level rise projections and time horizons:

| Shared Socioeconomic Pathways (SSP) | Description                                               | Time Horizons      | SLR Confidence |
|-------------------------------------|-----------------------------------------------------------|--------------------|----------------|
| SSP1-2.6                            | A low-emission scenario where global temperatures rise slightly | 2100               | Medium         |
| SSP2-4.5                            | A medium-emission scenario where global temperatures rise moderately | 2050, 2100         | Medium, Medium |
| SSP5-8.5                            | A high-emission scenario where global temperatures rise significantly | 2030, 2050, 2100   | Medium, Medium, Medium |
| High-end                            | Represents high-end but plausible outcomes of sea-level rise under a high-emission scenario | 2100, 2150         | Low, Low       |

**Return Periods**

The dataset estimates extreme sea levels for three return periods, representing the frequency of extreme events:

| Return Period (Years) | Description                                 |
|------------------------|---------------------------------------------|
| 1                      | Events expected once every 1 year.          |
| 100                    | Events expected once every 100 years.       |
| 1000                   | Events expected once every 1000 years.      |

**Ensembles**

Data is computed for seven statistical ensembles (1st, 5th, 17th, 50th, 83rd, 95th, and 99th percentiles) at two key timeframes: **2050 and 2100**. The projections stem from the **LISCOAST project**, a comprehensive study of coastal dynamics under climate change.

**Components of Flood Maps**

To estimate flood extent and depth under various scenarios for the entire coast of Europe, these maps integrate:  
- [x] Elevation data  
- [x] Manning roughness information inferred from land cover data  
- [x] Hydrodynamic simulations  
- [x] Climate information (hindcast and projections)

---

**How to Use the Flood Maps Data**

- [x] **Scenario selection**: select between return periods of extreme events of total water level (i.e., 1-yr, 100-yr, and 1000-yr), sea-level rise scenarios (i.e., SSP1-2.6, SSP2-4.5, SSP5-8.5, and high-end), or a combination of both.
- [x] **Ensemble selection**: select between the high, median, and low ensembles to understand the range of possible sea-level rise outcomes.
- [x] **Time horizon**: view flood maps for specific time slices (i.e, 2010, 2030, 2050, 2100, 2150).
- [x] **Current coastal defence level considered in the flood maps**: undefended (no protection beyond which is included in the DEM), minimum level of protection, or maximum level of protection.

---

**Model Outputs**

Pan-European flood maps covering different scenarios with their respective flood extent and depth.

![](./assets/Tool/FLOOD_outputs.png){ width=800 .center}

---

**Why Is This Data Important?**

TThe Flood Maps Data Layer in the CoCliCo platform allow assessment of risks, informing management strategies, and supporting decision-making for coastal planning, infrastructure protection, and emergency preparedness. Coastal flood maps provide a visual representation of potential areas at risk from flooding due to sea-level rise, extreme coastal storms, or both.

**Example of use**

> *"Using the Coastal Flood Maps, a marine conservation group identified several critical wetland areas at risk of being inundated due to storm surges combined with sea-level rise. The group used the flood extent and depth maps for different scenarios to prioritize restoration projects. By focusing on these vulnerable areas, they were able to implement targeted conservation efforts that helped protect the wetlands, preserving biodiversity and improving water quality for the surrounding community.”*

---

**Further Analysis**

The Flood Maps Data Layer in the CoCliCo platform provide a library of process-based flood simulations for the integrated scenarios. In the Workbench, these maps can be integrated with exposure information and vulnerability curves to estimate risks.

**References**

Copernicus. (2019). DEM - Global and European Digital Elevation Model. https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing- missions/collections-description/COP-DEM.

EEA, C. E. E. A. (2017). EEA coastline for analysis. https://sdi.eea.europa.eu/catalogue/srv/api/records/af40333f-9e94-4926-a4f0-0a787f1d2b8f.

Jamieson S, L’homme J, Wright G, Gouldby B (2012) Highly efficient 2D inundation modelling with enhanced diffusion-wave and sub-element topography. Proc. Inst. Wat. Man., 165(10): 581–595.

Sayers, P.B and Marti, (2006) RFSM - Rapid Flood Spreading Method - Initial Development. Developed as part of Floodsite and TE2100 by Marti and Sayers, HR Wallingford report for the Environment Agency.

Shchepetkin, A. F., & McWilliams, J. C. (2005). The regional oceanic modeling system (ROMS): A split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modelling, 9(4), 347–404. https://doi.org/10.1016/j.ocemod.2004.08.002

Stockdon, H. F., Holman, R. A., Howd, P. A., & Sallenger Jr, A. H. (2006). Empirical parameterization of setup, swash, and runup. Coastal engineering, 53(7), 573-588.

Sunamura, T. (1984). Quantitative predictions of beach-face slopes. Geological Society of America Bulletin, 95(2), 242-245.

Tolman, H. L. (2009). User manual and system documentation of WAVEWATCH-IIITM version 3.14. Technical Note, 3.14, 220. http://polart.ncep.noaa.gov/mmab/papers/tn276/MMAB_276.pdf%5Cnpapers2://publication/u uid/298F36C7-957F-4D13-A6AB-ABE61B08BA6B

van Maanen, N., De Plaen, J. J.-F. G., Tiggeloven, T., Colmenares, M. L., Ward, P. J., Scussolini, P., and Koks, E.: Brief Communication: Bridging the data gap – enhancing the representation of global coastal flood protection, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2024-137, in review, 2024.

Witjes, M., Parente, L., van Diemen, C. J., Hengl, T., Landa, M., Brodský, L., Halounova, L., Križan, J., Antonić, L., Ilie, C. M., Craciunescu, V., Kilibarda, M., Antonijević, O., & Glušica, L. (2022). A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000-2019) based on LUCAS, CORINE and GLAD Landsat. PeerJ, 10. https://doi.org/10.7717/peerj.13573.

Coastal Change Segments

Coastal erosion and flooding are known to be linked, with erosion potentially exacerbating flood extents and risk, but analysis of the combined hazards is limited. Coastal environments are characterised by various key factors including sediment supply, beach materials, underlying geology and human infrastructure and activity, which all influence the dynamic nature of the coastal zone.

The ‘Coastal Typologies and Erosion for Risk’ (CoasTER) database integrates existing information on erosion and other relevant coastal characteristics for Europe’s coastal floodplains. In particular, coastal areas where mobile sediments and coastal floodplains are co-located identifying the areas where erosion and flooding are most likely to interact. It also includes a coastal geomorphological typology which incorporates the influence of human modification in the form of hard engineering and infrastructure.

The Coastal Change Segments CoasTER database provides projections of global shoreline evolution under climate change. This assessment considers the combined effects of:

Ambient change: Historical shoreline trends.
Sea level rise (SLR): Based on RCP4.5 and RCP8.5 climate scenarios.
Storm-driven erosion: Instantaneous shoreline changes due to extreme events.

"Example"

“Around Europe, where has notable erosion occurred along coastal floodplains over recent decades?.”

Data Sources

The shoreline dataset for the analysis is that provided by the European Environment Agency (EEA, 2017). Other coastal characteristics were sourced as shown below.

Geomorphology and sediment type EEA/Eurosion (EEA, 2004); interpretation of World Imagery / Google Earth (Esri, 2024; Google Earth, 2024).

Land cover/use Corine Land Cover 2018 (CLC, 2020).

Indicative coastal floodplain extent (extreme tides, storms, 2m sea-level rise) CoCliCo flood units (Lincke & Hinkel, 2023).

Decadal shoreline movement trends (1984–2021) ShorelineMonitor+ (extended from Luijendijk et al., 2018).

Location of coastal structures (hard defences, other human infrastructure constraining shoreline evolution) EEA/Eurosion (EEA, 2004); interpretation of World Imagery / Google Earth (Esri, 2024; Google Earth, 2024).

Methods

The EEA shoreline was disaggregated into its component segments, which were then used as the basic unit for the database. A coastal zone extending 100m landward of the shoreline was defined as a representative landward extent and used for the identification of descriptive attributes. Attributes from the coastal characteristics data sources were added using spatial joins capabilities within the QGIS geographic information system software. Attributes were checked and amended, where appropriate, following visual interpretation of satellite imagery.

Important notes:

1. Indicative coastal floodplains were used in the database, scenario-based modelled flood extents are available and described in the Flood Maps Data Layer.
1. The historical shoreline movement trend is based on satellite measurements and is classified into three trends; erosion, accretion and stable. These broad classes identify rapidly eroding or accreting shorelines with slower changing shorelines classed as stable. The CoasTER database therefore provides broad shoreline movement trends rather than providing precise localised rates.

Climate Scenarios

The dataset includes projections for two climate scenarios

RCP4.5
A medium-emission scenario where global temperatures rise moderately.
RCP8.5
A high-emission scenario where global temperatures rise significantly.

Ensembles

Data is computed for seven statistical ensembles (1st, 5th, 17th, 50th, 83rd, 95th, and 99th percentiles) at two key timeframes: 2050 and 2100. The projections stem from the LISCOAST project, a comprehensive study of coastal dynamics under climate change.

How to Use the CoasTER database

Users can query the database using a range of attributes. For example:

Coastal classification: see the distribution of geomorphological classes
Hard engineering: see where defences and other structures influence coastal evolution.
Shoreline evolution: for the indicative coastal floodplains, see shorelines with recent rapid shoreline movement trends
Combine attributes: to further define areas of interest

Database Outputs

The CoasTER database considers the current situation around Europe. Its attributes can be queried for coastal analysis.

Why Is This Data Important?

The CoasTER database allows coastal areas where mobile sediments and coastal floodplains are co-located to be identified. It can be used as a preliminary diagnostic tool to identify areas where the interaction between flooding and erosion should be examined more closely, especially where significant erosion has been a recent trend and may continue into the future. It can also show where coastal structures and development may be affected by, and/or influence, future coastal sediment movements.

Coastal risk management: Identifying areas vulnerable to erosion and planning protective measures.
Infrastructure planning: Supporting long-term decision-making for sustainable coastal development.
Climate adaptation: Evaluating potential impacts of sea level rise and storm events.
Scientific research: Providing a robust dataset for studying coastal responses to climate change.

Example of use

"I would like to know where developed areas have coastal structures within 100m of the shoreline and have been subject to recent erosion and may need further management.”

Limitations

The CoasTER database is limited in coverage and accuracy by its source data. This has resulted in some counties and sections of the shoreline (e.g., inlets and small islands) not being included. It provides an interpretation of the coastal system as defined by the 2017 shoreline which omits more recent coastal developments such as port expansions and has digitizing artifacts that have not always been corrected.

Further Analysis

The CoasTER database provides a base to which further information relevant to the broadscale analysis of coastal floodplains can be added or updated as required.

References:

CLC. (2020). CORINE Land Cover 2018 (vector), Europe, 6-yearly - version 2020_20u1, May 2020. https://land.copernicus.eu/en/products/corine-land-cover/clc2018

EEA. (2004). Geology and geomorphology (EUROSION). Retrieved April 2023, from https://www.eea.europa.eu/data-and-maps/figures/geology-and-geomorphology

Esri. (2024). Esri World Imagery https://www.arcgis.com/home/webmap/viewer.html?webmap=50c23e4987a44de4ab 163e1baeab4a46

Google Earth. (2024). https://earth.google.com/web/

Lincke, D., & Hinkel, J. (2023). Report and final GIS layer of flood risk management units.

CoCliCo project deliverable 6.2. https://coclicoservices.eu/wp- content/uploads/2021/11/WP6_D6.2.Report-and-final-GIS-layer-of-flood-risk- management-units.pdf

Luijendijk, A., Hagenaars, G., Ranasinghe, R., Baart, F., Donchyts, G., & Aarninkhof, S. (2018). The state of the world’s beaches. Scientific Reports, 8(1), 6641. https://doi.org/10.1038/s41598-018-24630-6

Exposure and Vulnerability

Data Layers & User Stories

Risk and Adaptation

Data Layers & User Stories

Cost-Benefit Analyses

The Cost-Benefit Analyses User Story in the CoCliCo platform helps identify the most cost-effective ways to manage coastal flood risks under different climate scenarios. It evaluates three key adaptation strategies:

Protection – Building coastal defences like seawalls.
Retreat – Relocating people and assets away from flood zones.
Accommodation – Flood-proofing buildings to withstand extreme events.

The platform provides country-level insights on the best mix of these strategies, with more detailed local assessments available through the workbench. This helps policymakers and planners make informed, cost-effective adaptation decisions.

"I want to see economically optimal coastal adaptation options both today and, in the future, as well as the expected investments in coastal adaptation and the potential costs of flood damages"

Data Sources

The data flow of the cost-benefit model can be seen in this workflow chart. The output of the model (efficient adaptation pathways in the middle of the Figure) then feed onto the CoCliCo platform. Several parts or even components of this detailed Figure can be excluded to simplify it, e.g. dynamic programming, implementation of adaptation options, etc. The SSP/RCP scenarios inform the sea level rise (SLR) and vertical land motion (VLM) data.

Methods

The cost-benefit optimisation integrates several components:

A hazard component to model extreme sea level
An exposure component to assess population and assets at risk
A vulnerability component to assess the susceptibility of assets to hazards
An adaptation state space to outline potential adaptation pathways
Cost functions to estimate the costs associated with adaptation actions

The multi-stage cost-benefit optimisation is conducted for each of the 41,327 coastal floodplains individually. We consider a time horizon from 2020 to 2150 with 10-year time steps, a discount rate of 3% and three greenhouse gas emission scenarios: low emissions (SSP1-2.6), high emissions (SSP2-4.5) and very high emissions (SSP5-8.5).

Climate Scenario

The dataset includes projections for three climate scenarios:

SSP1-2.6
A low-emission scenario where global temperatures are limited to 1.5°C above pre-industrial levels, reflecting a sustainable future with rapid decarbonization.
SSP2-4.5
A medium-emission scenario where global temperatures rise moderately, reflecting a future with some efforts to mitigate climate change.
SSP5-8.5
A high-emission scenario where global temperatures rise significantly, reflecting a future with continued high greenhouse gas emissions and limited mitigation efforts.

Projections

The dataset includes projections up to the years 2050, 2100, and 2150, offering key metrics to inform decision-making for coastal resilience.

Adaptation Strategy

Protection
Raising coastal defenses.
Retreat
Managed withdrawal from vulnerable areas.
Accommodation
Implementing flood-proofing measures.
Protection & Retreat
A combined strategy where both approaches are efficient.
No Adaptation
Areas where adaptation measures are deemed inefficient.

The analysis accounts for adaptation costs, residual flood damages, and the most cost-efficient adaptation strategies for each floodplain.

How to Use the Data

Scenario Selection: Choose from the available scenarios ( RCP4.5, RCP8.5) to explore how Cost-Benefit Analysis may change under different climate futures.
Adaptation Strategy: Select an adaptation strategy between Protection, Retreat, Accommodation, Protection & Retreat adn No Adaptation
Projection: Choose between 2050, 2100, or 2150, depending on the timeframe for the adaptation strategy.

Model Outputs

For each coastal floodplain, the model determines the economically optimal adaptation pathway, which is a sequence of adaptation options over time. These adaptation pathways can be explored through the workbench.
The web viewer illustrates the proportion of the coastline where each adaptation option is economically optimal by 2150 for each country, based on the economically optimal coastal adaptation pathways for all 41,327 floodplains.

Why Is This Data Important?

This dataset is crucial for guiding cost-effective and sustainable coastal adaptation strategies under different climate change and socioeconomic scenarios. It helps:

Coastal decision-makers initiate proactive adaptation planning.
Global institutions estimate future adaptation costs at regional and national scales.
Researchers and policymakers assess the effectiveness of adaptation pathways over time.

Example of use

"National policymakers identified that significant funding would be required to address coastal flood risks in various regions. They allocated funding for coastal adaptation, encouraging local authorities to conduct their own research. This approach allowed local governments to assess specific risks and develop tailored solutions, ensuring more effective, region-specific adaptation strategies."

Limitations

This cost-benefit model uses broad data to manage computational limits, which means it doesn't include detailed information about properties, land use, or infrastructure. As a result, the model may be less accurate for specific floodplains. For example, it might suggest retreat as the best adaptation option for an area with a nuclear power plant, but this could present major challenges that the model doesn’t account for.

The model also only considers the median sea-level rise (SLR), leaving out high-end SLR scenarios. A sensitivity analysis showed that uncertainties in factors like the discount rate, protection and retreat costs, and protection levels have a bigger impact on the choice of adaptation options, the timing of actions, and total costs than the climate change scenarios themselves.

Further Analysis

Technical users can use the Workbench to perform detailed, localized analyses by adjusting variables like flood risks, cost factors, and adaptation options. This allows for tailored assessments of the most cost-effective strategies and the timing of actions at the local scale.

Users can explore different sea-level rise scenarios, test adaptation measures, and incorporate local data such as infrastructure details to refine their analysis. The Workbench enables deeper insights, helping inform more precise local adaptation strategies.

=== "Damage Costs"

The Damage Costs User Story in the CoCliCo aim at presenting the direct economical impacts of coastal flooding under different climate scenarios. While many coastal infrastructures are protected today, rising sea levels will increase flood extent and depth, leading to higher damage costs without further adaptation.

This tool helps policymakers and planners assess these costs at national, regional, and local levels in order to inform adaptation strategies. It combines the latest sea level projections, European flood hazard data, and infrastructure inventories to provide city-scale estimates of future flood damage.

> *"I need to quantify damage costs of infrastructures exposed to flooding and assess how it evolves under different climate change scenarios"*

**Data Sources**

Damage costs on infrastructures are calculated by combining three main components: **hazard**, **exposure**, and **vulnerability curves**.

- **Hazards**  
    - Source: CoCliCo project (data producer: IH-Cantabria; available on the CoCliCo STAC Catalog)  
    - Data: Flood maps (water depth) with or without defences  
    - Coverage: Hindcast, 2030, 2050, 2100, and 2150  
    - Scenarios: Permanent flooding, 1-year, 100-year, and 1000-year return periods under multiple SLR scenarios

- **Exposure**  
    - Source: Coastal European Exposure Database (data producer: Institute for Environmental Studies, Vrije Universiteit Amsterdam; available on the CoCliCo STAC Catalog)  
    - Classes: 11 infrastructure types – Building, Power, Wastewater, Telecom, Oil, Gas, Education, Healthcare, Rail, Road, and Water  
    - Geometry: Infrastructures represented as **points, lines, polygons, or multipolygons**

- **Vulnerability Curves**  
    - Source: *Physical Vulnerability Database for Critical Infrastructure Hazard Risk Assessments* (data producer: Institute for Environmental Studies, Vrije Universiteit Amsterdam)  
    - Data: Compilation of published vulnerability curves and associated costs  
    - Coverage: 102 vulnerability curves linked to 179 cost values  
    - Purpose: Characterize the **percentage of damage** to infrastructure as a function of **water depth**, differentiated by infrastructure type

![](./assets/Tool/DAM_DATA.png){width=900px .center}

**Methods**

The damage costs are calculated by intersecting hazard (raster) and exposure (polygons) data layers, and vulnerability curves. First, we overlay the coastal flood hazard map with infrastructure data to obtain an average water height for each infrastructure. Then, based on the category of the infrastructure, we apply the corresponding vulnerability curves (e.g. healthcare, education, railway, etc).

For the CoCliCo project, we used 18 different vulnerability curves. Once the damage is determined, the associated cost calculation is carried out: the damage cost is the product of the building's surface area, the percentage of damage, and the maximum damage, based on construction costs.

**How to Use the Data**

The Damage Cost data can be used to:

- [x] **Estimate economic impacts**: Quantify the direct financial losses from coastal flooding under different climate and sea-level rise scenarios.  
- [x] **Compare across scales**: Assess costs at municipal, regional, and national levels to understand where risks and damages are concentrated.  
- [x] **Support planning and investment**: Use damage estimates to prioritize adaptation strategies and allocate resources efficiently.  
- [x] **Overlay with exposure data**: Combine with building and infrastructure exposure layers for a more complete risk assessment.  
- [x] **Export and refine**: Download datasets for further analysis in the Workbench or other GIS tools.

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**Model Outputs**

Potential costs can be visualized in different ways: through a map that displays a color gradient based on the total damage cost for each LAU (Local Administrative Unit) across all infrastructures. To do this, the user selects the type of defense, the desired SSP scenario, the time horizon and the return period. The user can also view more details by clicking on a specific LAU. In this case, they can select the defense level, return period, and the category of infrastructure they are interested in. A graph will appear showing the evolution of costs over time, based on the different SSP scenarios.

Importantly, the costs presented here are the costs associated to a particular return period. It neither corresponds to the costs associated to a particular event that would affect a specific region, nor to an expected annual damage value that would integrate costs of various return periods.

![](./assets/Tool/flood_damage_output.png){ width=800 .center}

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**Why Is This Data Important?**

- [x] **Economic justification for adaptation**: Provides tangible cost estimates that can help policymakers argue for timely investments in resilience.  
- [x] **Supports strategic prioritization**: Highlights areas where damage costs are projected to be highest, helping decision-makers focus resources.  
- [x] **Complements exposure data**: Goes beyond identifying assets at risk by showing the financial implications of flooding.  
- [x] **Improves disaster preparedness**: Assists in planning for compensation mechanisms, insurance schemes, and recovery strategies.

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**Example of use**

> “National policy makers used the damage costs estimates to get a first order estimate of costs in regions and municipalities and prioritize on adaptation actions and investments in order to maximize the efficiency of public investments in adaptation. While this information can not be used as a single source of information to guide adaptation investments, it provides an element that can be considered together with additional evidence and selection criteria of decision makers”

> “The damage costs estimates at municipal level have been used to identify the potential damage costs in a particular flood plain, allowing to anticipate to what extent existing compensation mechanisms (e.g. insurance) are adequately designed to address loss and damages a now and in the future”

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**Limitations**

One of the main limitations of this method lies in the assumption that infrastructures remain unchanged over time, without considering any construction or destruction of infrastructure. Additionally, the lack of detailed information on certain infrastructures can affect the accuracy of selecting the vulnerability curve, which may lead to variations in the estimated cost. Similarly, the price used is an average price that does not account for specific factors such as the location of the damage or the current local construction costs.

Key Layers, Sources, and How to Interpret Them

Data Layers in the CoCliCo platform test

User Stories

Sea Levels

Natural Hazards

Exposure and Vulnerability

Data Sources

Critical Infrastructure Systems and Subsystems

Infrastructure Object Types

Methods

Geographic Coverage

How to Use the Data

Model Output

Why Is This Data Important?

Example of Use

Risk and Adaptation