To avoid damages and costs for the society due to flooding is it important to be able to model accurate rain scenarios. Due to climate change is it thought that there will be more heavy rainfalls in the future which will increase the risk for flooding. This master thesis will therefore look at the parameters that affect the risk for flooding with focus on raincloud movement and peak arrivals. Earlier research has shown that different directions of the rain will affect the flooding risk differently. Generally will a rain that has the same direction as the downstream flow lead to a higher risk for flooding. The peak arrival time has also shown to lead to different results were a late peak arrival seems to lead to a higher risk for flooding. There is usually too little time and costly to test different movement and peak arrival scenarios, so this report will focus on which of these parameters that have the greatest flooding impact and largest internal variation.  MIKE 21 (created by DHI) is the software used to model the different rain scenarios. The rain types used to test the different scenarios will be a uniform rain, a traditional CDS-rain and five rain shapes that are based on empirical rain types created by SMHI.  The shape of the empirical rains is based on several studies from measured rain events with weather radar. Weather radar have become a fundamental tool in weather forecasting because it can collect data in near real time and also measure the spatial variation inside the rainfall.  These seven mentioned rain types will not be moving, and the rain will have the same spatial intensity over the study area. They will be compared both to each other but also to a CDS-rain that will move over the study area in 4 different directions (north-south, east-west, south - north and west - east).  The different directions gave all very similar results while there was a larger difference between the rain types with different peak arrivals. The peak-value seems to be an important factor when it comes to flooding risk based on the results in this report. The constant uniform rain had the lowest amount of flooded areas while the CDS-rain (which had the highest peak) affected the area the most.

Pluvial floods are formed directly from surface runoff after extreme rain events. Urban areas are prone to suffer from these floods due to large portions of hardened surfaces and limited capacity in the stormwater infrastructure. Previous research has shown that catchment response is influenced by the spatio-temporal behaviour of the rainstorm. A rainstorm moving in the same direction as the surface flow can amplify the runoff peak and temporal variability of rainfall intensity generally results in greater peak discharge compared to constant rainfall. This research attempted to relate the effect of storm movement on flood propagation in urban pluvial flooding to the effect from different distributions of rainfall intensity. An additional objective was to investigate the flood response from recent findings on the temporal variability in Swedish rain events and compare it to the flood depths produced by a CDS-rain (Chicago Design Storm), where the latter is the design practice in flood modelling today. A 2D surface model of an urban catchment was coupled with a 1D model of the drainage network and forced by six different hyetographs. Among them were five empirical hyetographs developed by Olsson et al. (2017) and one a CDS-rain. The rainstorms were simulated to move in different directions: along and against the surface flow direction, perpendicular to it and with no movement. Maximum flood depth was evaluated at ten locations and the model results show that storm movement had negligible effect on the flood depths. The impact from the movement was likely limited by the big difference in speed between the rainstorm and the surface flow. All evaluated locations showed a considerable sensitivity to changes in the hyetograph. The maximum flood depth increased at most with a factor of 1.9 depending on the hyetograph that was used as model input. The CDS-rain produced higher flood depths compared to the empirical hyetographs, although one of the empirical hyetographs produced a similar result. Based on the results from this case study, it was concluded that storm movement was not as critical as the temporal variability of rainfall when evaluating maximum flood depth. / Pluviala översvämningar skapas från ytavrinning vid intensiva nederbördstillfällen. De uppstår ofta i urbana miljöer till följd av den höga andelen hårdgjorda ytor och ledningsnätets begränsade kapacitet. Forskning har visat att ett regnmolns rörelseriktning och hastighet påverkar avrinningsförloppet. Om molnet rör sig längs med flödesriktningen i terrängen kan en ökning i vattenlödet nedströms ett avrinningsområde uppstå. Denna effekt har visat sig vara störst om hastigheten hos regnmolnet och vattenflödet är likvärdiga. Ytterliggare en faktor som påverkar avrinningsförloppet är hur regnintensiteten är fördelad över tid. Olsson et al. (2017) har tagit fram fem empiriska regntyper som speglar tidsfördelning inom ett Svenskt regntillfälle. Inom översvämningsmodellering är det vanligt att använda ett så kallat CDS-regn (Chicago Design Storm), vilken har en given tidsfördelning. Med anledning av detta är det intressant att jämföra översvämningar genererade av ett CDS-regn och av de empiriska regntyperna. Syftet med denna studie var att utreda hur regnmolns rörelse påverkar urbana pluviala översvämningar med avseende på vattendjup, samt att jämföra denna påverkan med effekten från olika tidsfördelningar av regnintensiteter. En kombinerad dagvattenmodell (1D) och markavrinningsmodell (2D) av en mindre svensk tätort användes för att simulera olika regnscenarier. De fem empiriska regntyperna och ett CDS-regn simulerades med en rörelseriktning längs med, emot och vinkelrätt i förhållande till flödesriktningen. Även scenarier med stationära regnmoln simulerades. Maximala översvämningsdjup utvärderades i tio punkter spridda över hela modellområdet. Resultatet från simuleringarna visade att regnmolnets rörelse hade försumbar påverkan på översvämningsdjupen. De olika tidsfördelningarna av regnintensitet hade däremot betydande påverkan på de maximala översvämningsdjupen. Som mest var det det maximala översvämningsdjupet 1.9 gånger större beroende vilken regntyp som användes som indata. CDS-regnet genererade i regel de största översvämningsdjupen, även om utfallet från en av de fem empiriska regntyperna var förhållandevis likvärdigt. Regnintensitetens tidsfördelning var därmed en kritisk parameter vid den hydrauliska modelleringen av urbana pluviala översävmningar, till skillnad från molnrörelse som hade försumbar påverkan.