Site icon Coclico Services

How to use the Flood Maps User Story (#2)

suromao

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

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:

  1. Sea Level Rise Projections
  2. Flood Maps
  3. Building Exposure
  4. Projections of Exposed People
  5. Damage Costs of Exposed Infrastructures
  6. Adaptation Based on Cost-Benefit Analysis

In this e-guideline, we walk you through the Flood Maps User Story.

2. Introduction to Flood Maps User Story

The Flood Maps User Story in the CoCliCo platform helps users understand and prepare for coastal flooding. They are essential for assessing vulnerability, informing risk management strategies, and supporting decision-making for coastal planning, infrastructure protection, and emergency preparedness. These maps show areas at risk of flooding due to rising sea levels, coastal storms, or both. They combine data on land elevation, water movement, and climate predictions to estimate how floods might impact different coastal areas in Europe. This collection of flood maps serves as the basis for other User Stories. 

3. Step-by-Step Platform Usage

  1. Access the CoCliCo Platform:
    1. On the left-hand menu bar, navigate to the “Natural Hazards” category, then “Inundation distribution during flood events” under “User Stories”.  
  2. Select a Scenario:
    1. Choose a combination of defence level for the flood maps, a TWL return period event, a climate scenario (e.g., SSP1-2.6, SSP2-4.5, SSP5-8.5 or high end), and time horizon. 
  3. Analyze Visualizations:
    1. Use interactive tools to zoom into regions and access localized insights. 
    2. Use the “Add to Dashboard” feature to retain charts and graphs for further comparison and analysis 
    3. 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.  
  4. Further Analysis:
    1. Export maps or raw datasets for further analysis in the Workbench or other GIS tools​​. 

4. Target Users & Intended Use

Target Users:

Intended Use:
The user story is intended to guide the development and implementation of coastal flood maps by ensuring they meet the needs of decision-makers and stakeholders. The layer provides clear, reliable visualizations for identifying coastal areas at risk from flooding due to the combination of relative sea-level rise and extreme weather events. 

Key Benefits:

5. Example of Use 

“Using the Coastal Flood Maps, a marine conservation group identified several critical wetland areas at risk of being flooded due to storms combined with relative 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.”

6. Data, Methods, and Model Overview

Data Sources:

To map flood risks across Europe, we used detailed topographic data to represent the land. This included a high-resolution (25-meter) digital elevation model (DEM) from Copernicus (2019), a defined coastline to set boundary conditions from the European Environment Agency (EEA, 2017), and land-use data from Witjes et al. (2022), which was translated into Manning’s roughness coefficients to estimate how water moves across different surfaces.

To understand how ocean forces contribute to flooding, we analyzed water levels at 1 km intervals along the European coast using two approaches. For permanent flooding scenarios, we used data from the CoCliCo Regional Sea Level Rise (SLR) Projections. For temporary flood events, we used extreme total water level (TWL) scenarios based on a reconstructed TWL hindcast. This hindcast included tidal data from the TPXO database, storm surge simulations from the ROMS model (Shchepetkin & McWilliams, 2005), and wave setup estimates based on downscaled wave conditions modeled with WaveWatch III (Tolman, 2009).

Finally, we incorporated data on coastal flood protection measures developed by Vrije Universiteit Amsterdam (van Maanen et al., 2024) to improve the accuracy of the flood maps, ensuring they reflect existing defenses along Europe’s coasts.

Methods:

The methodology for mapping coastal flood risks followed three main steps: 

1. Defining the Coastal Floodplain 

We identified flood-prone areas as coastal regions between 0 and 15 meters in elevation, hydraulically connected to the sea. These areas were divided into 22 flood units, each with detailed topographic meshes. The meshes consisted of irregular impact zones that followed natural terrain features, with smaller 25-meter impact cells (based on the DEM). Each impact zone was assigned a Manning roughness coefficient based on dominant land use. 

2. Constructing Flood Scenarios 

For permanent flooding, hydrographs were created by combining sea level rise (SLR) projections with the mean spring high tide at each location. For episodic flooding, hydrographs were based on extreme total water level (TWL) analysis and storm duration estimates. The TWL hindcast was reconstructed by summing: 

The peak over threshold (POT) method was used to identify extreme TWL events, estimating return levels with an exponential model. Future flood scenarios combined relative SLR with TWL returns values. 

3. Running Coastal Flood Simulations 

We used the RFSM-EDA 2D flood model (Jamieson et al., 2012) for large-scale flood simulations, incorporating terrain details. The Saint-Venant equations were applied to compute water flow between impact zones, and flood depth was calculated for each 25-meter impact cell. 

Final flood maps showed depth and extent for different scenarios along the European coast. These maps were post-processed to account for existing coastal defences, considering policy-based protection levels at the provincial level (NUTS 2). 

Model Outputs:

Limitations:

The flood maps provided in the platform offer a robust and homogeneous distribution of flood extent and depth under different scenarios for the coast of Europe considering the spatial variability of its marine dynamics and floodplain characteristics. However, working with large-scale studies entails assumptions and simplifications in order to achieve a homogeneous analysis. As such, the resulting maps provided serve as guidance and should not be used for local-scale interventions and adaptation planning. 

The maps without defences (undefended maps) provide the upper limit of coastal flooding. The maps with defences (defended maps) provide the lower limit of coastal flooding. Importantly, defended maps should be treated with caution as they assume that the whole province is protected with the same level of protection. 

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. 

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/uuid/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 

Exit mobile version