# Region Skåne - Co- Innovation Lab 2-Helsingborg
## Introduction and Objectives of the Climate Risk Assessment
### Regional context.
[Helsingborg Municipality](https://en.wikipedia.org/wiki/Helsingborg_Municipality), located in the Skåne region of southern Sweden, includes urban, peri-urban, and agricultural landscapes shaped by a network of small streams, culverts, and historical wetlands. The municipality and its upstream catchments face increasing risks from pluvial flooding, high flows, and extreme rainfall events, driven by changing climate patterns. Urban expansion and infrastructure have fragmented ecological corridors, reducing connectivity between key ecological value cores such as wetlands, swamp forests, and deciduous forests. At the same time, protected areas, including Natura 2000 sites, and existing ecological assets provide opportunities for targeted Blue-Green Infrastructure ([BGI](https://www.sciencedirect.com/science/article/abs/pii/S0040162520313548)) interventions. These can mitigate flood risks, restore hydrological continuity, and strengthen ecological resilience at the catchment scale.
*Figure 1 - Location of the Helsingborg municipalitiy s in Skåne.*
### Scope of the tutorial.
This tutorial presents a replicable Climate Risk Assessment (CRA) workflow for identifying optimal locations for Blue-Green Infrastructure (BGI) in Helsingborg. The focus is on nature-based measures—such as [two-stage ditches](https://knowledge4policy.ec.europa.eu/publication/ritobacken_en), floodplains, and [wetland restoration](https://climate-adapt.eea.europa.eu/en/metadata/case-studies/urban-storm-water-management-in-augustenborg-malmo) (Figure 2) that can both mitigate flooding and enhance ecological connectivity. The GIS-based approach combines hydrological and ecological spatial datasets, including flood hazard layers, depressions, historical wetlands, and ecological value cores, to identify multifunctional priority areas. The method supports integration of BGI into local and regional adaptation strategies, providing decision-makers with spatially explicit, evidence-based guidance for restoration planning under current and future climate conditions.
* **Disclaimer**
> This tutorial is intended as a general workflow example and does not replace software-specific documentation (e.g., GIS, hydrological, hydraulic tools user/technical manuals). Users should already be familiar with the relevant geospatial data formats, data pre-processing techniques, and modelling concepts applicable to their hazard of interest (e.g., flood mapping.), as well as with the specific input/output requirements and run functionalities of the modelling software before attempting to replicate this workflow
*Figure 2 - Illustration of two-stage ditches in Helsingborg and vegetation in a ditch (top) , and Blue-green corridors in the agricultural landscape in Helsingborg municipality (bottom)*
### CRA objectives.
The Helsingborg CRA aims to:
* **Identify priority sites** for BGI that address both pluvial and fluvial flood risks while improving ecological connectivity.
* **Link ecological value cores**—including wetlands, swamp forests, and deciduous forests—through restored corridors along streams and culverts.
* **Integrate hydrological and ecological criteria** to select multifunctional intervention areas, prioritising locations with overlapping flood vulnerability and restoration potential.
* **Support planning and policy** by providing spatial evidence for land-use strategies, climate adaptation plans, and ecological restoration projects.
* **Enable replication** of the workflow in other regions by relying on GIS-based analysis and datasets that have open or EU-wide equivalents.
### Intended Users
The CRA results are intended for local and regional policymakers, municipal planners, and environmental authorities responsible for climate adaptation and ecological management in Helsingborg. They provide spatial guidance for:
* Strategic land-use planning and flood risk reduction.
* Implementation of targeted nature-based solutions and restoration projects.
* Integration of ecological connectivity into urban and regional planning frameworks.
Stakeholders such as water management agencies, conservation organisations, and infrastructure planners can also use the outputs to prioritise interventions that deliver combined hydrological and ecological benefits.
## Flood mitigation and Ecological Resilience – Helsingborg
### Description and context
Helsingborg and its upstream catchments are exposed to pluvial flooding and high flows during extreme rainfall events, which threaten urban areas, agricultural land, and ecological assets. The combination of impervious urban surfaces, altered watercourses, and fragmented ecological corridors increases runoff and reduces natural retention capacity. Historical wetlands, low-lying depressions, and designated floodplain restoration areas offer opportunities to restore hydrological functions. Strategic Blue-Green Infrastructure (BGI) interventions—such as two-stage ditches, floodplain reconnection, and wetland creation—can mitigate flood risks while enhancing ecological connectivity between key value cores. This dual focus supports climate adaptation by improving water balance and strengthening landscape-scale ecological network. This includes mapping hazard-prone areas, evaluating current and potential connectivity of habitats, and prioritising interventions that deliver both climate adaptation and biodiversity benefits.
| | | | |
| ---------------------------- | ----------------------------------------------------------------------------------------- | ------------------- | ----------------------------------------------------------------------------------------------------- |
| **Dimension** | **Indicator(s)** | **Unit** | **Purpose** |
| Flood extent and water depth | Flood extent and water depth, linked to event intensity (return period, climate scenario) | km², m | Identify flood-prone areas, inform mitigation strategies, and assess changes after BGI implementation |
| Ecological connectivity | Overlap zones between flood-risk areas and ecological value cores / linkages | N/A, visual overlay | Locate multifunctional zones where BGI can reduce flood impacts and improve habitat connectivity |
*Table 1 – key indicators tracked.*
### Data sources and tools
The CRA uses high-resolution **topographic, hydrological, and ecological datasets** from regional and national geoportals, complemented by thematic data from specialised agencies. Core inputs include LiDAR-based DEMs (Figure 3), rainfall and flow statistics, flood hazard maps, historical wetland inventories, ecological value core layers, and Natura 2000 boundaries. Flood modelling tools support data processing and visualization, both proprietary and free to use to foster replication in other contexts.
The table below summarizes key datasets, their role in the workflow, and potential open or EU-wide equivalents for transferability:
| **Data Type** | **Source** | **Role in Workflow** | **Open/EU Alternative** |
| ---------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| LiDAR-based DEM | Regional/[National Geoportal](https://www.lantmateriet.se/sv/geodata/vara-produkter/produktlista/markhojdmodell-nedladdning/) | Detect surface depressions, analyse micro-topography for flood accumulation and ecological connectivity | 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) |
| Rainfall map by return period | [VattenAtlas](https://vattenatlas.se/) | Input for pluvial flood modelling and hazard mapping | ERA5 [extreme precipitation indicators](https://cds.climate.copernicus.eu/datasets/sis-european-risk-extreme-precipitation-indicators?tab=overview) |
| Extreme and maximum flow data | [MSB](https://gisapp.msb.se/Apps/oversvamningsportal/hemta-data.html) (Swedish Civil Contingencies Agency); [VattenAtlas](https://vattenatlas.se/) | Peak river flow estimation under design scenarios | Copernicus Climate Dat store [“Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections”](https://cds.climate.copernicus.eu/datasets/sis-hydrology-variables-derived-projections?tab=download) |
| Historical wetlands | [VattenAtlas](https://vattenatlas.se/) | Identify potential restoration areas and historical flood retention zones | Copernicus [Land Cover 2020 (raster 10 m), global, annual - version 1](https://land.copernicus.eu/en/products/global-dynamic-land-cover/land-cover-2020-raster-10-m-global-annual), includes wetlands delineation |
| Potential floodplain restoration | County Administrative Board | Support planning for natural flood retention measures | No EU-wide equivalent (context-specific) |
| Water connectivity links | County Administrative Board | Identify hydrological corridors and link ecological value cores | [EU-Hydro River Network Database 2006-2012 (vector), Europe](https://land.copernicus.eu/en/products/eu-hydro) |
| Catchment boundaries | County Administrative Board | Provide spatial reference for hydrological and ecological analysis | [EU-Hydro River Network Database 2006-2012 (vector), Europe](https://land.copernicus.eu/en/products/eu-hydro) |
| Ecological value cores – deciduous forest, swamp forest, wetland | County Administrative Board | Locate existing high-value ecosystems for connectivity planning | Copernicus High Resolution Layer: High Resolution Layer Tree Cover and Forests
Copernicus Land Cover 2020 (raster 10 m), global, annual - version 1, includes wetlands delineation
|
| Natura 2000 sites | [National Geoportal](https://ext-webbgis.lansstyrelsen.se/naturvardesoversikt/) | Identify legally protected ecological zones | EEA Natura 2000 Database ([vector, Europe-wide](https://www.eea.europa.eu/data-and-maps/data/natura-14/natura-2000-spatial-data)) |
*Table 2 – used data, an alternative dataset to replicate the assessment outside the study area, when available*
* **Climate change effects in hydrological extremes**
> *Although the current workflow does not incorporate official climate projections, a first approximation of future flood hazards, if not available from local studies, may be derived using open datasets from the Copernicus Climate Data Store. These include bias-adjusted impact indicators such as daily river discharge extremes under different climate scenarios (e.g. RCP 8.5).*
>
> *A practical example of this approach has been implemented* [*here*](https://www.euspa.europa.eu/newsroom-events/success-stories/copernicus-hydropower-flood-assessments) *for a hydropower site in Switzerland, where Copernicus climate projections, coupled with limited gauging station statistics for basic downscaling, were used to estimate future 100-year flood discharges and run simplified hydraulic simulations to assess potential downstream impacts. The methodology explores how to use Copernicus datasets to create a preliminary “climate-adjusted” flood map.*
*Figure 3 – example of Lidar DTM, one of the main driver of flood risk assessment, for Helsingborg area compared to satellite map; obstacles such as buildings and trees are filtered from the DTM surface.*
The workflow combines proprietary and open-source **tools** for GIS processing, flood hazard analysis, and hydrological-ecological connectivity mapping. Proprietary platforms are used for advanced analysis and visualisation, while open-source alternatives have been identified so that the method can be replicated in other contexts:
| | | |
| ---------------------------------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Tool** | **Type** | **Role in Workflow** |
| [ArcGIS Pro](https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview) | Proprietary | GIS platform for overlaying ecological and flood layers, digitising connectivity corridors, and producing BGI priority maps |
| [QGIS](https://qgis.org/) | Open | Open alternative for overlaying ecological and flood layers, digitising connectivity corridors, and producing BGI priority maps |
| [Scalgo Live](https://scalgo.com/) | Proprietary | Visualises surface water accumulation, identifies depressions and overland flow paths under heavy rainfall or high flow scenarios. Generates base flood hazard layers (e.g. 100-year rainfall, low points, extreme flows) used to delineate flood-prone areas |
| [SaferPlaces](https://saferplaces.co/) | Proprietary | Visualises surface water accumulation, identifies depressions and overland flow paths under heavy rainfall or high flow scenarios. Generates base flood hazard layers (e.g. 100-year rainfall, low points, extreme flows) used to delineate flood-prone areas |
| [TauDEM](https://hydrology.usu.edu/taudem/taudem5/) | Open | Topographic analysis for flow paths and watershed delineation |
| [HEC-RAS](https://www.hec.usace.army.mil/software/hec-ras/download.aspx) | Open | Hydraulic modelling for flood scenarios, estimation of flood extent and water depth |
*Table 3 – used tools and role in the workflow, when available a free similar alternative to proprietary solutions is provided.*
### Methodology
#### Step 1 - Data acquisition and preparation
The first stage of the workflow consists in gathering and **preparing all spatial datasets** needed for the analysis, ensuring that they share the **same projection** (identified by a unique EPSG code , like [3006](https://epsg.io/3006) for the example) **and are clipped** to the extent of the area of interest( e.g. Helsingborg Municipality( including and its upstream contributing catchments.
High-resolution **topography** from a LiDAR-based DEM (Figure 2) provides the basis for identifying surface depressions, micro-topography features, and potential flow accumulation zones; if unavailable, the Copernicus DEM can serve as an alternative.
Hydrological inputs include rainfall maps for different return periods and extreme flow statistics from sources (such as MSB and Vattenatlas), which define the magnitude and frequency of flood scenarios. Where available, official flood hazard maps—showing extent and depth for selected return periods—should be loaded; otherwise, they can be produced using compatible hydraulic or hydrological modelling tools.
**Ecological datasets**, including value cores for wetlands, swamp forests, and deciduous forests, as well as Natura 2000 sites and **water connectivity links** from the County Administrative Board, are then added to capture biodiversity and connectivity priorities. Layers showing historical wetlands and designated floodplain restoration areas are included to identify locations with natural retention potential. (Figure 4).
The **contributing catchment boundaries** provide a reference framework for structuring the analysis at hydrological unit level. Before moving forward, all layers should be checked for resolution consistency, format compatibility, and completeness of attributes. When national datasets are not available, equivalent open or EU-wide sources listed in the data table can be used to replicate the process.
* **Domain extent**
> *It should be noted, however, that adopting the full contributing catchment as model domain may result in an **excessively large computational extent.** In such cases, it is advisable to first perform a hydrological assessment of peak discharges / flood waves in the modelled river, so that the hydraulic risk model can be restricted to the areas actually affected by flooding and targeted for Nature-Based Solutions. This approach avoids unnecessarily large DEM inputs and reduces computational load without compromising the relevance of the analysis.*
*Figure 4 - example map showing value cores and connectivity map obtained via GIS overlaying of key layers.*
#### Step 2 - Model setup and run.
Once spatial data are in place, the key task is to **simulate flood processes** to locate where hydraulic risk overlaps ecological opportunity. The procedure begins with **terrain morphology analysis to pinpoint depressions, low-lying storage zones, and overland main flow pathways** easily activated during high-flow conditions. This can be achieved using hydro morphological tools that compute flow direction, accumulation, and potential storage segmentations.
These analyses are directly applicable in simplified, zero-dimensional (0D) modelling approaches, where storage zones and flood retention areas are identified based solely on terrain input—no need for complex hydraulic simulations. Such 0D methods are often employed to represent, beside simplified flood models, also rapid, intense rainfall events (e.g., urban flash floods), and are useful when computational resources are limited or rapid assessments are needed (\[1]).
Example of depression delineating using terrain morphology analysis in reported in (Figure 3)
*Figure 5 – example of depression detection via raster-based analysis of DTM (courtesy of* [*SaferPlaces*](https://saferplaces.co/)*)*
The initial morphological analysis already provides valuable insight into potential intervention areas. By extracting flow directions and delineating depressions or temporary storage zones from the DEM, it is possible to generate maps of flow paths and inundation-prone areas. When these outputs are overlaid in the GIS environment, they offer a first spatial indication of where Blue-Green Infrastructure could be located to maximise retention capacity and align with natural hydrological processes (Figure 6).
*Figure 6- example of Flow paths and flood zones detection via Flood Modeling tools, overlayed in GIS environment to base maps.*
Next, flood hazard layers representing extent and water depth for target return periods (e.g., 10-, 50-, 100-year events).
If hydraulic simulation is carried out, the choice of modelling approach should reflect both the availability of topographic information and the computational resources at hand. In some cases, a very simple scheme—such as the zero-dimensional (0D) approach introduced earlier, based on storage areas and flood routing—may be sufficient. Where higher detail is possible, the analysis can instead rely on more refined methods, such as a two-dimensional inundation model of the river channel and adjacent flood-prone areas.
*Figure 7 - Example of 2D flood model of a flood volume upstream of an urban area (courtesy of* [*SaferPlaces*](https://saferplaces.co/)*)*
#### Step 3: Ecological connectivity mapping and priority area selection
At this stage, the analysis shifts from purely hydrological aspects to the i**ntegration of ecological structures.** The first step is to combine the official **ecological value cores—such as wetlands, swamp forests, and deciduous forests—with the water connectivity links** provided by the County Administrative Board. This allows the identification of existing ecological nodes and corridors across the landscape. Where gaps exist, new potential connections can be drawn manually along streams, ditches, or culverts, thereby sketching a conceptual network that could restore or reinforce continuity.
The next step is to ove**rlay this ecological layer with the flood-related outputs prepared in Step 2.** Particular attention is given to areas where depressions, historical wetlands, or high-flow zones overlap with ecological corridors, since these locations represent multifunctional opportunities: they can serve as flood retention areas while simultaneously strengthening habitat connectivity.
Priority **should be given to polygons where several favourable conditions coincide**—for example, significant storage potential in a depression, proximity to existing ecological value cores, or alignment with known flood pathways. These areas could then be **digitised into a dedicated GIS layer**, (e.g. “BGI intervention opportunities.”)
Each polygon can be characterized with **basic attributes such as** approximate storage volume, connectivity function, or degree of overlap with hazard zones.
The outcome of this step is a **spatially explicit shortlist of candidate sites where Blue-Green Infrastructure measures are most likely to deliver combined benefits** for flood mitigation and ecological resilience. This dataset will serve as the foundation for testing hydraulic performance and ranking interventions in the following step.
#### Step 4 – Hydraulic Re-Simulation and Performance Assessment of Proposed BGI Sites
Once candidate intervention areas have been identified, the next step is to test their potential effectiveness in reducing flood risk. This is done by **incorporating** the proposed Blue-Green Infrastructure (BGI) polygons—such a**s wetlands, two-stage ditches, or restored floodplains—into the hydraulic simulation domain prepared in Step 2.** Each intervention area is assigned **basic hydraulic properties**, for example an indicative storage volume, infiltration capacity, or connectivity to adjacent flow paths. These attributes are simplified but sufficient to represent the effect of the intervention within the chosen modelling framework.
The hydraulic model is then re-run under the same flood scenarios previously simulated, this time including the BGI elements. The outputs are compared against the baseline to quantify changes in flood extent and depth. Ty**pical performance indicators include the reduction in flooded area, the decrease in maximum water depth within a buffer zone, or the total volume of water retained.** These results can be stored as attributes within the GIS layer of BGI polygons, creating a spatially explicit database of intervention benefits.
This procedure may be repeated for multiple return periods or climate scenarios to evaluate the robustness of BGI measures under varying conditions. The outcome is a ranked set of candidate sites, where hydrological performance can be weighed together with ecological connectivity benefits. Such basic multi-criteria assessment supports decision-makers in prioritising those interventions that deliver the highest combined resilience gains.
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