Abstract
AbstractThis paper describes a framework in which artificial extreme sea levels (ESLs) can be generated for use in flood risk analyses. Such analyses require large numbers of events to accurately assess the risk associated with certain return water levels and quantify uncertainties surrounding the temporal variability of ESL events. Stochastic models satisfy this requirement as they are computationally inexpensive, and thus, many thousands of events may be generated over a very short period of time. As a case study, we have developed a stochastic model for the German Baltic Sea coast capable of simulating the temporal behavior of ESLs. To do this, high‐resolution water level data from 45 tide‐gauges have been used as model input. At each location, observed ESLs are identified and parameterized. Artificial ESLs are generated using Monte Carlo simulations based on the parametric distribution functions fitted to the parameterized observed ESLs. We show that the method outlined here provides an accurate representation of ESLs at all tide‐gauges tested. However, the model is limited by the availability, length, and quality of high‐resolution water level data. Due to the rarity of ESLs in the German Baltic Sea, including historical measurements into the stochastic procedure allows for the generation of artificial ESLs more in‐line with past extremes.
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