Abstract
Extreme value analysis seeks to assign probabilities to events which deviate significantly from the mean and is thus widely employed in disciplines dealing with natural hazards. In terms of extreme sea levels (ESLs), these probabilities help to define coastal flood risk which guides the design of coastal protection measures. While tide gauge and other systematic records are typically used to estimate ESLs, combining systematic data with historical information has been shown to reduce uncertainties and better represent statistical outliers. This paper introduces a new method for the incorporation of historical information in extreme value analysis which outperforms other commonly used approaches. Monte-Carlo Simulations are used to evaluate a posterior distribution of historical and systematic ESLs based on the prior distribution of systematic data. This approach is applied at the German town of Travemünde, providing larger ESL estimates compared to those determined using systematic data only. We highlight a potential to underestimate ESLs at Travemünde when historical information is disregarded, due to a period of relatively low ESL activity for the duration of the systematic record.
Highlights
Since the mid 20th century losses from natural hazards have been trending upwards as a result of physical and socioeconomic changes (Okuyama and Sahin, 2009)
The method outlined in this paper provides a simple approach to incorporate historical extremes into extreme value analysis (EVA), allowing for reduced uncertainties in the estimates of extreme sea levels (ESLs) and better representation of historical outliers
The question arises whether the historical and systematic data sets can be reconciled, or has there been some change in the generation mechanisms of ESLs at Travemünde which renders the historical records no longer representative? 360 A fundamental assumption of EVA is that the population of extremes is stationary, and while methods to model nonstationary extremes exist, they are not yet capable of incorporating historical information as the duration of observation is not defined
Summary
Since the mid 20th century losses from natural hazards have been trending upwards as a result of physical and socioeconomic changes (Okuyama and Sahin, 2009). 2.1 Extreme Value Analysis Extreme events are commonly referred to by their return period, which defines an average or expected interval between exceedances of a given magnitude, typically in years (Coles et al, 2001). The purpose of detrending water level data before conducting EVA is twofold; first, a fundamental assumption of extreme value theory is that the sampled extremes are stationary (Coles et al, 2001); and second, changes in water levels such as 85 those induced by climate change can be adjusted so that the sampled data reflects current conditions (Arns et al, 2013). Uncertainties surrounding future extremes are typically dealt with through the inclusion of a climate surcharge (MELUR, 2012; StALU MM, 2012) 95
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
