Design Life Level: Quantifying risk in a changing climate

Holger Rootzén, Richard W. Katz

Water Resources Research · 2013 · 221 citations · 24 references

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Concepts

TL;DR

Return levels and periods have long guided engineering design, but they rely on a stationary climate assumption that fails under climate change, and current risk management typically uses single‑year high‑quantile thresholds. This paper introduces the Design Life Level, a nonstationary‑climate risk metric, and examines how to quantify the statistical uncertainty in estimating its high‑quantile values. The Design Life Level is defined as the upper quantile (e.g., 5%) of the distribution of the maximum hydrologic variable over a specified design‑life period (e.g., 2015–2064), and the authors relate this definition to existing literature while demonstrating its computation and application.

Abstract

[1] 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.

References

24