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Design Life Level: Quantifying risk in a changing climate

Artikel i vetenskaplig tidskrift
Författare Holger Rootzén
R. W. Katz
Publicerad i Water Resources Research
Volym 49
Nummer/häfte 9
Sidor 5964-5972
ISSN 0043-1397
Publiceringsår 2013
Publicerad vid Institutionen för matematiska vetenskaper, matematisk statistik
Sidor 5964-5972
Språk en
Länkar dx.doi.org/10.1002/wrcr.20425
https://gup.ub.gu.se/file/146603
Ämnesord design criteria, climate change, nonstationary, extreme value statistics, return level, return period, UNITED-STATES, TEMPERATURES, STATIONARITY, MANAGEMENT, EXTREMES, FRANCE
Ämneskategorier Klimatforskning

Sammanfattning

In the past, the concepts of return levels and return periods have been standard and important tools for engineering design. However, these concepts are based on the assumption of a stationary climate and do not apply to a changing climate, whether local or global. In this paper, we propose a refined concept, Design Life Level, which quantifies risk in a nonstationary climate and can serve as the basis for communication. In current practice, typical hydrologic risk management focuses on a standard (e.g., in terms of a high quantile corresponding to the specified probability of failure for a single year). Nevertheless, the basic information needed for engineering design should consist of (i) the design life period (e.g., the next 50 years, say 2015-2064); and (ii) the probability (e.g., 5% chance) of a hazardous event (typically, in the form of the hydrologic variable exceeding a high level) occurring during the design life period. Capturing both of these design characteristics, the Design Life Level is defined as an upper quantile (e.g., 5%) of the distribution of the maximum value of the hydrologic variable (e.g., water level) over the design life period. We relate this concept and variants of it to existing literature and illustrate how they, and some useful complementary plots, may be computed and used. One practically important consideration concerns quantifying the statistical uncertainty in estimating a high quantile under nonstationarity.

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