Inferring rare events: The role of the length of observations

Abstract

Extreme value analyses (EVA) are often used to determine the frequency of extreme events. The length of the available observations is an important aspect when performing EVA. It is generally known that more available data results in better estimates with less uncertainties. The main objective of this research report was to assess what the influence of the length of the observations is when inferring rare events. This was done by first analyzing the sensitivity of inferred return levels from synthetic data from three known distributions. Also, three case studies were analyzed to observe the sensitivity of inferred return levels and return periods from the observations. The results of the analyses were that a larger sample size generally leads to a higher confidence in the estimates of inferred return levels from synthetic data. However, there will always remain some uncertainty associated with the estimates. The confidence in the inferred return levels from observations also generally increase for an increasing sample size. However, this can not always be observed and other aspects can be more dominant than an increasing sample size. No correlation could be observed between the sample size and the inferred return periods. The conclusion of the research was that, while there is a positive correlation between the sample size and the confidence in the estimates, there will always remain some uncertainties. It is therefore important to always communicate the uncertainties associated with estimates.