Floods is a serious concern across Europe due to the enormous material damage and death toll. Of alltypes of floods, flash floods and large-scale river floods have become major natural hydrological hazardsin most countries. The city of Halmstad was chosen due to its placement on the southern west coast ofSweden, a region for which climate projections have indicated more precipitation and potential forflooding. In recent years a number of floods have also been observed with associated damages. Usingextreme value analysis on observed data these events can be interpreted in terms of return level valuesand their frequency of occurrence. The seasonal variation of the precipitation and discharge of thecatchment were analyzed based on 43-year precipitation and 25-year discharge observation data and therelationship to NAO index was investigated to give a preliminary overview of the hydrologicalconditions in Halmstad and its causes. The results showed that Halmstad was seasonally characterizedby high discharge in winter and lower discharge in summer with the highest rainfall. The effect of stormtracks represented by the NAO index on the precipitation and discharge in winter months was evident.This study focused on the analysis of extreme data of precipitation and discharge. The return levels forup to 50-year return period were estimated by GEV fitting. The estimated return level of discharge fora 50-year return period is 250 m³/s, and the return levels of precipitation for a 50-year flood was foundto be 68 mm/day. Two cases were selected from a compiled annual maxima discharge data set foranalyzing and comparing their weather conditions based on ERA5 data. The results showed that differentweather conditions do have an impact on the total rainfall, and there were similar patterns but largedifferences between ERA5 reanalysis data and observed SMHI data was also shown emphasizing theneed for long-term observational data sets and further evaluation of reanalysis data.

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Increased extreme precipitation has been documented in many regions around the world, in- cluding central and northern Europe. Global warming increases average temperature, which in turn enhances atmospheric water holding capacity. These changes are believed to increase the frequency and/or intensity of extreme precipitation events. In determining the design storm, or a worst probable storm, for infrastructure design and failure risk assessment, experts commonly assume that statistics of extreme precipitation do not change significantly over time. This so- called notion of stationarity assumes that the statistics of future extreme precipitation events will be similar to those of historical observations. This study investigates the consequences of using a stationary assumption as well as the alternative: a non-stationary framework that con- siders temporal changes in statistics of extremes. Here we evaluate stationary and non-stationary return levels for 10-year to 50-year extreme precipitation events for different durations (1-day, 2-day, ..., 7-day precipitation events), based on the observed daily precipitation from Sweden. Non-stationary frequency analysis is only considered for stations with statistically significant trends over the past 50 years at 95% confidence (i.e., 15 to 39 % out of 139 stations, depend- ing on duration, 1-day, 2-day, ..., 7-day). We estimate non-stationary return levels using the General Extreme Value distribution with time-dependent parameters, inferred using a Bayesian approach. The estimated return levels are then compared in terms of duration, recurrence in- terval and location. The results indicate that a stationary assumption might, when a significant trend exists, underestimate extreme precipitation return levels by up to 40 % in Sweden. This report highlights the importance of considering better methods for estimating the recurrence in- terval of extreme events in a changing climate. This is particularly important for infrastructure design and risk reduction. / Ökad extrem nederbörd har dokumenterats globalt, däribland centrala och norra Europa. Den globala uppvärmningen medför en förhöjd medeltemperatur vilket i sin tur ökar avdunstning av vatten från ytor samt atmosfärens förmåga att hålla vatten. Dessa förändringar tros kunna öka och intensifiera nederbörd. Vid bestämning av dimensionerande nederbördsintensiteter för byggnationsprojekt antas idag att frekvensen och storleken av extrem nederbörd inte kommer att förändras i framtiden (stationäritet), vilket i praktiken innebär ingen förändring i klimatet. Den här studien syftar till att undersöka effekten av en icke-stationärt antagande vid skattning av dimensionerande nederbördsintensitet. Icke-stationära och stationära nerderbördsintensiteter föråterkomsttider mellan 10 och 100år bestämdes utifrån daglig och flerdaglig svensk nederbörds- data. Nederbördintensiteterna bestämdes med extremvärdesanalys i mjukvaran NEVA, där den generella extremvärdesfördelningen anpassades till årlig maximum nederbörd på platser i Sverige som påvisade en ökande trend under de senaste 50åren (15% till 39 % utav 139 stationer, beroende på varaktighet). De dimensionerande nederbördsintensiteterna jämfördes sedan med avseende på varaktighet, återkomsttid och plats. Resultaten indikerade på att ett stationärt antagande riskerar att underskatta dimensionerande nederbördsintensiteter för en viss återkomsttid med upp till 40 %. Detta indikerar att antagandet om icke-stationäritet har större betydelse för olika platser i Sverige, vilket skulle kunna ge viktig information vid bestämning av dimensionerande regnintensiteter.

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