Integrating Risk and Resilience Approaches to Catastrophe Management in Engineering Systems

TLDR

Recent natural and man‑made catastrophes have renewed interest in resilience, defined as the capacity of complex engineering systems to adapt without catastrophic loss, though it is sometimes framed as the probability of exceeding a tipping point. The authors argue that treating resilience solely as a risk metric conflates resilience and risk perspectives and propose a different conceptualization. They define resilience as an emergent property arising from a recursive process of sensing, anticipation, learning, and adaptation rather than a static system attribute. They show that resilience cannot be assessed only from component parts, that resilience analysis is distinct yet complementary to risk analysis, and illustrate this with the 2011 Mississippi River Basin flooding case.

Abstract

Recent natural and man-made catastrophes, such as the Fukushima nuclear power plant, flooding caused by Hurricane Katrina, the Deepwater Horizon oil spill, the Haiti earthquake, and the mortgage derivatives crisis, have renewed interest in the concept of resilience, especially as it relates to complex systems vulnerable to multiple or cascading failures. Although the meaning of resilience is contested in different contexts, in general resilience is understood to mean the capacity to adapt to changing conditions without catastrophic loss of form or function. In the context of engineering systems, this has sometimes been interpreted as the probability that system conditions might exceed an irrevocable tipping point. However, we argue that this approach improperly conflates resilience and risk perspectives by expressing resilience exclusively in risk terms. In contrast, we describe resilience as an emergent property of what an engineering system does, rather than a static property the system has. Therefore, resilience cannot be measured at the systems scale solely from examination of component parts. Instead, resilience is better understood as the outcome of a recursive process that includes: sensing, anticipation, learning, and adaptation. In this approach, resilience analysis can be understood as differentiable from, but complementary to, risk analysis, with important implications for the adaptive management of complex, coupled engineering systems. Management of the 2011 flooding in the Mississippi River Basin is discussed as an example of the successes and challenges of resilience-based management of complex natural systems that have been extensively altered by engineered structures.