GIS-Based Flood Susceptibility Mapping in Skåne County, Sweden : A Multi-Criteria Decision Analysis Approach

Sammanfattning

Floods are one of the most common natural hazards and they can cause serious risk to people, infrastructure, and the environment. Because of this, it is important to identify areas that are more probable to be flooded. The aim of this study is to investigate flood susceptibility using Geographic Information Systems (GIS) and a Multi-Criteria Decision Analysis (MCDA) approach in the Skåne County, Sweden. The study also produces a flood susceptibility map as the final output and determines which municipalities have the highest percentage of flood susceptible areas. In this research, a Multi-Criteria Decision Analysis (MCDA) approach is used to analyze different factors that affect flooding. The main factors in this study include elevation, slope, rainfall, land use/land cover, soil type, distance from waterbodies, height above nearest drainage (HAND), and topographic wetness index (TWI). The Analytical Hierarchy Process (AHP) is used to calculate the importance of each factor by comparing them with each other and assigning weights. All factor layers are prepared and standardized in GIS and then combined using a weighted overlay method to produce the final flood susceptibility map. Three AHP scenarios were created to evaluate how different weighting choices affect the final result. The scenarios were compared with an independent floodplain reference map covering Skåne County to assess their performance. The validation results showed that all three scenarios produced similar flood susceptibility patterns, but Scenario 1 achieved the highest spatial overlap with the independent floodplain reference layer and was therefore selected as the final model. The final map is classified into five classes, from very low to very high flood susceptibility. After that, the flood susceptibility classes are summarized for each municipality in Skåne. The percentage of moderate, high, and very high susceptibility areas is calculated for every municipality. The result shows that Lomma has the highest percentage of flood susceptible areas, followed by Åstorp and Staffanstorp. This means that lowland areas and municipalities close to waterbodies generally show higher flood susceptibility in this model. This information can support better spatial planning, flood risk management, and decision-making in Skåne, especially in municipalities with higher flood susceptibility.