# Region Funen - Co-Innovation Lab 2- Vejrup
## Introduction and Objectives of the Climate Risk Assessment
### Regional context.
The [Vejrup Å catchment](https://www.sdu.dk/en/forskning/arcadia/about-arcadia/arcadia-in-funen/lab2), located in eastern Funen within [Odense](https://english.odense.dk/) and Municipality (with smaller parts in [Kerteminde](https://en.wikipedia.org/wiki/Kerteminde_Municipality) and [Faaborg-Midtfyn](https://www.fmk.dk/)), is mainly exposed **to coastal flooding from Odense Fjord**.
The area north of Kerteminde road is undergoing major land-use transitions: agricultural land is being converted into commercial developments, while low-lying coastal zones are designated for wetland and salt marsh restoration.
Municipal climate adaptation strategies, as set out in the [**Odense Climate Adaptation Plan**](https://www.odense.dk/byens-udvikling/klima/klimatilpasning/klimatilpasningsplan) (Figure 1), foresee the withdrawal of coastal protection up to the Kerteminde road. This retreat strategy is promoted within the framework of the [**Ministry for the green transition**](https://mgtp.dk/groent-danmark/english-a-greener-denmark) and is intended to create space for restored wetlands and salt marshes. These measures are expected to mitigate coastal flooding, reduce nutrient runoff to the fjord, and support biodiversity.
Figure 1 - Climate risk assessment map from Odense Municipality climate adaptation plan 2024.
This approach reflects a broader institutional shift toward adaptive land-use planning, where flood risk management, ecological restoration, and urban development are coordinated to reduce **coastal squeeze**, cut CO₂ emissions, and provide multiple benefits for local communities and ecosystems.
### Scope of the tutorial.
This tutorial describes a replicable Climate Risk Assessment (CRA) workflow for the Vejrup Å catchment, addressing coastal flood hazards caused by sea level rise and storm surges. The workflow uses high-resolution elevation data, storm surge scenarios, coastal flood simulations, and land-use information to map current and future inundation zones.
The approach supports local adaptation planning by testing both nature-based and traditional solutions identified in the catchment, such as wetland restoration, salt marsh creation, and coastal protection structures. These interventions are assessed for their capacity to reduce inundation extent and depth while enabling sustainable land-use transitions. Designed for transferability, the tutorial combines national datasets with open European sources, allowing replication in other coastal lowlands facing similar risks.
* **Disclaimer**
> This tutorial is intended as a general workflow example and does not replace software-specific documentation (e.g., GIS, hydrological, or coastal modelling tools user/technical manuals). Users should already be familiar with the relevant geospatial data formats, data pre-processing techniques, and modelling concepts applicable to flood hazard assessment, as well as with the specific input/output requirements and run functionalities of the modelling software before attempting to replicate this workflow.
### CRA objectives.
The CRA for the Vejrup Å catchment aims to:
* Identify areas exposed to **coastal flooding** from sea level rise and storm surges under different return periods and climate projections.
* Map inundation zones using high-resolution topography and coastal flood simulations to assess the extent and depth of flooding.
* Highlights were **wetland restoration** and **salt marsh creation**, together with coastal protection structures, can reduce flood risk.
* Support **municipal planning** by linking flood hazard maps with land-use strategies and by indicating zones suitable for business development or nature restoration.
* Provide a spatial basis for future assessments of **economic damage**, moving from hazard mapping toward damage-based evaluation. *In this tutorial, this component is addressed only at a qualitative level through overlay of land-use with flood maps.*
### Intended users.
The CRA results are primarily addressed to **municipal planners and officers** in Odense, Kerteminde, and Faaborg-Midtfyn, who are responsible for integrating flood risk into land-use and climate adaptation strategies. They are also relevant for regional decision-makers engaged in frameworks such as *the*, where spatial strategies for adaptation are evaluated.
Stakeholders involved in the ongoing **land-use transition**—such as the conversion of agricultural land into wetlands, salt marshes, or commercial districts—can use the outputs to guide interventions and assess their feasibility. In addition, **civil protection services** and **infrastructure managers** benefit from **hazard maps** that identify vulnerable assets and support the prioritisation of protective measures.
## Flood Hazard – coastal.
### Description and context
The Vejrup Å catchment is mainly exposed to coastal flooding from Odense Fjord, driven by sea level rise and storm surges. Large low-lying areas north of Kerteminde road are particularly vulnerable and have been designated for restoration into wetlands and salt marshes under the **Odense Climate Adaptation Plan**.
Coastal flood events threaten agricultural land, planned commercial districts, and critical infrastructure, with risks expected to increase under projected climate scenarios (IPCC RCP 8.5).
To address these challenges, local strategies promoted through [**The ministry of the green transition**](https://mgtp.dk/groent-danmark/english-a-greener-denmark) envisage the withdrawal of coastal protection to Kerteminde road and the use of nature-based solutions—such as wetland and salt marsh creation—combined with structural defences where necessary ([Odense fjord coastal council 2024](https://odensefjordsamarbejdet.dk/wp-content/uploads/2024/09/Analyser-og-anbefalinger-Kystvandraadet-for-Odense-Fjord.pdf), Figure 2). The overall aim is to “make room for nature facing the fjord,” reducing flood risk while supporting biodiversity, nutrient retention, and CO₂ reduction.
Figure 2 - Wetland restoration maps for wetland scenario 1\* and 2\*\*. Note that the two layers are overlapping, and wetland scenario 2 is slightly smaller than wetland scenario 1.
\*Scenario 1: Conversion of all croplands to extensive grassland (6.700 ha new wetlands)
\*\*Scenario 2. Conversion of all land uses except for urban land within the floodplain delineated by QSWAT+ to wetlands.
This tutorial therefore focuses on the workflow for assessing coastal flooding, mapping hazard zones, and exploring the effectiveness of adaptive measures in reducing inundation extent and depth.
| | | | |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------ | -------------------------------------------------------------------------------------------------------- |
| **Dimension** | **Indicator(s)** | **Unit** | **Purpose** |
| Flood extent & water depth | Flooded area and maximum water depth under different return periods (e.g. 10, 20, 100 years; with climate projection for 2100). Water depth also provides the basis for typical damage assessment, which in this tutorial is only mentioned qualitatively and not developed further. | km², m | Identify flood-prone zones, quantify severity, and classify exposure levels |
| Flood damage (qualitative) | Exposure of different land-use categories to flooding; potential damage estimated either as flooded area by land-use class (m²) or via depth–damage curves if available | m² by land-use type, or qualitative damage class | Provide a first approximation of potential damages and support integration with external economic models |
Table 1 – key indicators tracked-Flood Hazard
### Data sources and tools
Flood risk assessment in the Vejrup Å catchment is based on high-resolution topography, storm surge scenarios, hydrological forcing, and land-use datasets provided by national authorities. These datasets allow the simulation of coastal and inland flooding under current and projected conditions. Where local datasets are not available, equivalent open European sources can be used to ensure transferability of the workflow to other regions.
| **Data type** | **Source (national)** | **Role in workflow** | **Open/EU alternative** |
| ---------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| LiDAR-based Digital Terrain model (0,4 m grid) | [GeoDanmark](https://dataforsyningen.dk/data/930) | Elevation base for flood modelling; identifies depressions and flow paths | Copernicus DEM - Global and European Digital Elevation Model [(open – raster 30m, 10m for selected users)](https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM) |
| Storm surge and sea level rise (Table 3). | Local/national studies, IPCC RCP scenarios (variable return periods 5, 10, 20, 50, 100 and climate projections) | Defines marine flooding scenarios under current and climate change | Copernicus Climate Dat store Climate indicators for Europe from 1940 to 2100 derived from reanalysis and climate projections
Global sea level change indicators from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections
|
| Hydrological adaptation structures | [ArealDataDanmark](https://arealdata.miljoeportal.dk/datasets/urn:dmp:ds:kystbeskyttelsesanlaeg) | Representation of dikes, pumping stations, and barriers in models | None (site-specific) |
| Building footprints | [GeoDanmark](https://dataforsyningen.dk/data/2677) | Exposure analysis; identification of at-risk assets | OpenStreetMap building layer ([vector, global)](https://osmbuildings.org/) |
| Land use / land cover | [GeoDanmark](https://dataforsyningen.dk/data/996) | Classification of flood-prone areas by land-use category for exposure and Damage assessment | Copernicus Urban Atlas
CLCplus Backbone
|
Table 2 – used data, an alternative dataset to replicate the assessment outside the study area, when available
* **Storme surge and Climate change effects**
> *The storm surge scenarios are selected following the IPCC 8.5 climate prognosis for a return period of 5, 10, 20, 50 and 100 years in 2022 and 2100. The sea level rise (in cm) is estimated for each scenario in the following table:*
>
> 
Table 3 - Overview of storm surge data applied for risk assessment.
T**he flood hazard workflow** for the Vejrup Å catchment relies on a combination **of GIS platforms and flood modelling tools** (Table 4). GIS software provides the core environment for integrating elevation, land-use, and exposure datasets, producing flood maps and supporting spatial analysis.
For the simulation of coastal flooding from storm surge and sea level rise, both proprietary and open-source tools can be used.
The choice of tool depends on data availability, licensing, and the level of detail required, but in all cases the outputs include flood extent and depth maps that support municipal adaptation planning and evaluation of mitigation measures.
| | Type
| Role
|
| ---------------------------------------------------------------------------------------- | ---------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| | Open
| Core environment for GIS analysis, map production, overlay of hazard and exposure layers
|
| [Scalgo Live](https://scalgo.com/) | Proprietary
| Simulate surface water dynamics and map flood-prone areas across the catchment
|
| [SaferPlaces](https://saferplaces.co/) | Proprietary
| Simulate surface water dynamics and map flood-prone areas across the catchment, include damage model
|
| | Open
| Open-source hydrodynamic model for simulating coastal inundation (storm surge, tsunami, wave run-up).
|
Table 4 – used tools and role in the Coastal Flood Hazard workflow, when available a free similar alternative to proprietary solutions is provided.
### Methodology
#### Step 1 - Data acquisition and preparation
High-resolution LiDAR-based DEM (0.4 m, Figure 3) from national geoportal forms the base for flood simulations. Coastal protection features (dikes, pumping stations) and building footprints from the same source may be added to refine topography and catch actual status.
* **Note on DEM resolution.**
> When using very high-resolution LiDAR DEMs (e.g. 0.4 m), the data can become too heavy for hydraulic modelling. A practical solution is to down sample to 1–2 m resolution, which is still reasonable for flood modelling. This can be done through mosaicking and resampling in GIS software (e.g. QGIS) or by using command-line tools such as GDAL. For example:
>
> gdalwarp --config GDAL\_NUM\_THREADS ALL\_CPUS -r bilinear -tr 1,2 1,2 -t\_srs EPSG:25832 -co COMPRESS=LZW -co BIGTIFF=YES -co PREDICTOR=2 c:/folder1/\*.tif c:/folder1/mosaic.tif
>
> For further reference, see the GDAL [*gdalwarp documentation*](https://gdal.org/en/stable/programs/gdalwarp.html).
 
Figure 3– example of Lidar DTM along the coast from GeoDanmark geoportal (down sampled @ 1,2 m ) compared to satellite map, obstacles such as buildings and trees are filtered from the terrain surface. Underwater topography is not represented.
High-resolution LiDAR-based DEM (0.4 m) from SDFI provides the elevation base for coastal flood simulations. Hydrological forcing is defined by **storm surge and sea level rise scenarios** following IPCC RCP 8.5 for 2022–2100, with return periods of 5, 10, 20, 50, and 100 years (Table 3). These scenarios set the boundary conditions for marine inundation.
**Land-use layers** (agriculture, urban, restoration areas), together with building footprints and municipal planning data, are added in GIS to represent ongoing conversions into business districts or nature restoration (wetlands and salt marshes) and actual “ante operam” status.
**Coastal protection structures** such as dikes and levees, along with critical infrastructure, must also be included in the model.
Where no ready-to-use geospatial layers exist, these features can be digitized manually in GIS to ensure that protective capacity is correctly represented in the flood simulations.
Depending on the platform, they may also be overlaid directly onto the DEM, with the exact format adapted to the input requirements and capabilities of the chosen flood simulation tool.
#### Step 2 – Model setup and run.
The coastal flood model is configured using the prepared DEM, storm surge levels, and sea level rise increments. Using the flood modelling tool of choice (Table 4), storm surge return periods (e.g. 5, 10, 20, 50, 100 years) ,eventually worsened to consider sea level rise projections in climate change conditions (Table 3),are combined to delineate areas below the water thresholds (Figure 4)
Building footprints, land-use layers, and coastal protection structures are integrated in the simulation to ensure that exposure and defences are correctly represented.
Model outputs typically include raster-vector classified flood maps by depth range (e.g. <25 cm, 25–50 cm, 50–100 cm, >100 cm) and by return period (Figure 4, Figure 5) showing inundation extent and water depth:
These maps allow a first evaluation of coastal flood hazard, and they form the basis for the subsequent overlay with land use and planning data in step 3.
Figure 4 – example of large, flooded area with 10 cm marine water by a 100-year storm surge in 2100 in Odense Fjord catchment area.
 
*Figure* *5 – example of detailed costal flood inundation model for actual (T100, 1,72 m a.s.l. ) and projected (T100 2100, 2,52 m a.s.l. ) surge conditions ,(courtesy of* [*SaferPlaces*](https://saferplaces.co/rimini-and-climate-change-the-added-value-of-the-sea-park-parco-del-mare/) *platform).*
Figure 6 - Workflow to obtain flood risk map using one of the flood models listed in Table 2
#### Step 3 – Analysis and interpretation
Flood extent and depth maps generated in Step 2 are imported into GIS for post-processing and interpretation. The results are overlaid with land-use layers, municipal planning zones, and building footprints to identify which categories of land and infrastructure are most affected under different storm surge scenarios.
The classification of flood depth ranges (e.g. <25 cm, 25–50 cm, 50–100 cm, >100 cm) supports the identification of vulnerability hotspots. By intersecting hazard outputs with agriculture, commercial areas, residential zones, and planned restoration sites, it is possible to estimate which surfaces and assets are exposed and also generate simple spatial statistics as the key indicators mentioned in Table 1
Figure 7– example of overlay of basic land use-cover map with flood areas in climate change scenarios in GIs environment.
Although a full damage model is not implemented in this tutorial, the analysis provides a **qualitative assessment of potential flood damage**, based on the overlap between flood maps and land-use categories. This forms the basis for future extensions where depth–damage curves could translate hazard into economic loss estimates.
* **Note: damage assessment.**
> *Note all modelling suites can be complemented with damage estimation modules. These typically require intersecting flood hazard outputs (e.g. water depth rasters) with asset value layers and applying water depth–damage curves. This can be performed, when not embedded in the flood assessment tool, separately within the GIS environment or via external models.*
>
> *For a more advanced implementation of this methodology, including regionalized vulnerability functions, see for example:*
>
> \*\*\*Cost-benefit analysis of coastal flood defence measures in the North Adriatic Sea\
> \**Amadio, M., Essenfelder, A.H., Bagli, S., …Mysiak, J., Roberts, S.*\
> \&#xNAN;*Natural Hazards and Earth System Sciences, 2022, 22(1), pp. 265–286,* [*https://doi.org/10.5194/nhess-22-265-2022*](https://doi.org/10.5194/nhess-22-265-2022)
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