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Extreme value statistics seeks a relation between the magnitude of an extreme event and its probability ofoccurrence. Here we are interested in the largest daily precipitation event in a year: the annual maximum of daily precipitation amounts (in liter per square meter collected from 6am UTC to 6am UTC the next morning). The magnitude of this largest annual event varies from year to year and with a probability of 1% (p=0.01), the annual maximum exceeds the values given in the map. us on average only in one out of 100 years (1%), we observe an event of this size. is frequency interpretation of probability motivated the term "100-year event" and the map thus shows the 100-year return-level.A popular analogy is rolling a dice which shows "6" on average only once out of 6 trials. However, rolling itonly 6 times does not guarantee having exactly once a "6" among the trials. However, whenincreasing the number of trials to, say 6000, we are very likely to have 1000 rolls showing a "6" (as stated by the law of large numbers). In this sense, it is also not guaranteed to observe exactly one event exceeding the magnitude of an 100-year event in 100 years. However, increasing the number of trials in analogy to the dice experiment, we are more likely to observe during 1000 years 10 events of the 100-year event size.e cartoon suggests that these events might cluster in time, i.e. we observe two events exceeding the 100-year events in a 100-year period and none in the next 100-year period. Although this eect is an important eect, it is not addressed in the frame of the approach presented here.
In the wake of changing hydro-climatological, geo-physical and socio-economic conditions the magnitude, frequency and impact of certain types of natural hazards are likely bound to change as well. This is highly of utmost importance for many regions in the world where risks due to natural hazards have to be managed and mitigated and this is where the research training group “Natural hazards and risks in a changing world (NatRiskChange)” aims to foster the scientific knowledge basis. This research training group started on October 1st 2015 and is funded by the Deutsche Forschungsgemeinschaft DFG. The central goal of NatRiskChange is to pursue the development of methods to improve hazard and risk analysis and quantification based on the transient, non-stationary nature of hazards and risks in response to changing natural and anthropogenically altered components of the Earth system. Key scientific aims are the development, testing, and pilot application of studies on identification, quantification (mechanisms) and prediction of transient natural hazards and associated risks.
Within NatRiskChange, a telephone aided survey was conducted in October and November 2017 among companies, which were affected from heavy rainfall or flash flood events in 2014 to 2016. We aim at gathering information about the companies experiences with severe weather warning systems, the type and extent of the damage as well es the state of recovery. Results shall identify improved mitigation measures for the management of eavy rainfall events. We thank all participants of the survey for their support!