## 2 Methods  
The initial phase of the city analysis involved gathering archival data on past flooding and extreme hydrological events. Access to a wealth of archival data and scientific sources provided ample information on historical floods resulting from intense rainfall, snowmelt, or riverbank overflow. Given the cyclical nature of certain hydrological events that recur every few decades or centuries and short memory of residents, this data shed sometimes new light on flood-prone areas and the severity of flooding. Furthermore, current data on street and building flooding were collected, drawing from interventions by fire-fighting services. This dataset included the date of the event along with precise coordinates. The effectiveness of this initial phase of analysis may vary, as the availability of archival data on historical flooding and precise coordinates of local flood events is often limited or inconsistent. The spatial analysis involved localizing non-drained urban basins based on the digital elevation model, flooding prone areas based on the non-drained basins and maps of risk flooding, localisation of impervious and green areas. The following parameters has been also calculated: Landsat Normalised Difference Vegetation Index (NDVI) indicator, ratio of green areas to area of the city, retention potential factor based on green areas, Normalised Difference Built-Up Index (NDBI) in urban areas. The spatial analysis of the non-drained areas was conducted based on the 5 m resolution digital elevation model. The flood-prone areas were delineated by identifying regions that are not intersected by any natural or artificial drainage systems capable of facilitating water runoff. Limitations of this method include the fact that the digital elevation model (DEM) represents a specific point in time. In areas where dynamic land movement occurs, such as subsidence or ongoing urban development, the DEM may contain inaccuracies. Moreover, urban environments often feature engineered water retention systems or temporary storage areas that may not be captured in the DEM but can still influence surface runoff. Additionally, this method does not account for key hydrological parameters such as soil permeability and infiltration capacity. The inherent errors and resolution limitations of the DEM itself must also be considered. Additionally, spatial analyses were conducted using data from the Informatic System of National Protection (ISOK), accessible via the information platform. This dataset includes flood risk assessments for events with return periods of 10 and 100 years, respectively. Impervious areas were identified based on the Imperviousness Density 2018 dataset (Raster 10 m and 100 m), downloaded from the Copernicus Land Monitoring Service. Furthermore, the average soil sealing and the intensity of soil sealing were calculated. The main limitation of these methods is data accuracy. Soil sealing data is derived from a dataset prepared for 2018, yet this parameter changes dynamically due to urbanisation and land-use alterations. Additionally, flood risk assessments depend on multiple models and scenario-based simulations, which may introduce uncertainties. The resolution and temporal relevance of these datasets, as well as potential errors in underlying hydrological and climatic assumptions, can further affect the reliability of the analysis. The primary indicator for the city, in the context of mitigating the effects of severe climate change, is the ratio of green areas to the total city area. The green components used to calculate this index are derived from the following classes of objects within the land cover (LC) category: forested and wooded areas, shrub vegetation, permanent crops, and grass vegetation. This is expressed by the following equation:  
\[\text{green area resource index}=\frac{\text{green areas}}{\text{area of the city}}*100\ [\%]. \tag{1}\]The next indicator offers insights into the proportion of green spaces within the urban area.