Floods are one of the most common types of natural disasters. They annually affect vast amounts of people and cause severe economic losses. While fluvial, coastal, and flash floods are well studied, pluvial floods (rain related) have received modest attention from researchers and decision-makers in comparison. There are several reasons for this, one is that it has been considered a fixed problem with infrastructure and other engineered solutions and another is that they are generally undramatic and small-scale. However, as cities expand, the environment’s ability to retain and dispose of excess water is inhibited and as the frequency of extreme weather events is expected to increase due to climate change, the risk associated with pluvial floods has become increasingly recognized. Commercial and open-source Urban pluvial flood models tend to require advanced modelling expertise, considerable computational power, large amounts of input data and are often expensive. Consequently, there is less knowledge of flood inundation caused by pluvial floods compared to other types. This thesis investigates the Bluespot model, which aims to provide an approachable tool to generate an overview of the effects of pluvial floods in urban areas. The model requires few input data and is relatively simple to perform. Results from the model are compared to the August 2021 flood event in Gävle, Sweden.The study finds that results ranged from accurate to over- and underestimated. Slope and incoming water were found to affect the outcome most. Blue spots without the influence of streams or other waterways, with a distinct slope were mapped with accuracy and showed consistency with coarser resolutions. Consequently, underpasses in the road network were mapped with especially good consistency. Further, blue spots within close distance to large flow accumulation were underestimated and the accuracy tended to decrease with a coarser resolution. The model cannot account for water outside blue spots, thus, when large volumes of water accumulate and spread beyond these boarders it generates poor results. These areas were found to be efficiently indicated by generating a heatmap from high-flow accumulation points. Thus, indicating low confidence and where a hydraulic flood model should be performed. Depending on the scope a 1-3m resolution is recommended for investigating effects on property etc and a 5-10m resolution is sufficient for investigating underpasses, however, a finer resolution will generate more accurate results.