Erik Porse, PhD

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Resilience in Engineered, Natural, and Water Systems


The term resilience emerged in ecology during the early 1970s. At the time, researchers were debating the existence of equilibrium points in ecosystems. Equillibriums were considered stable configurations of species that an ecosystem could “evolve” (or succeed) to reach. Beyond that, little change would occur. Ecology literature accepted the existence of such system-wide, globally-stable states (Lewontin 1969). In this view of stable ecosystems, natural resource managers calculated maximum sustainable yields, which were consistent extractions of resources that a system could regularly provide without collapsing (Schaefer 1954).

Research in population biology challenged the view that ecosystems achieved long-term equilibrium (Sutherland 1974). In time, research recognized that ecosystems have periods of instability, which are linked to system complexity and disruption from external effects (Pimm 1984). These insights were seminal. Holling (1973) proposed that ecosystems move between periods of stability and change, describing “resilience” in ecosystems as the ability of the system “to absorb changes of state variables, driving variables, and parameters, and still persist.” Stability, on the other hand, was “the ability of a system to return to an equilibrium state after a temporary disturbance.” Linear systems, or non-linear systems close to a local stability point, can often be treated as having a global optimum (Holling 1986; Rogers & Fiering 1986; Pimm 1991). But disturbances can cause unexpectedly large changes. Ecosystems transition over time through multiple configurations of species composition and interaction (Klein et al. 2003; Blackmore & Plant 2008) and may remain in either local or global points of stability (Ludwig et al. 1997). The possible set of states of the ecosystems themselves can even be dynamic (Peterson et al. 1998; Folke et al. 2002, 2010).

More recently, the evolving concept of resilience theory has examined and critiqued human management of natural resource systems. Most policies consider engineering resilience , which seeks regulated performance to minimize deviations from a desired target, which supports economic planning. In contrast, managing for ecological resilience recognizes that systems are not stable. Disturbances can alter the structure and function of systems, possibly leading to a new operational state (Holling 1996). For instance, an ecosystem may be permanently transformed from a forest to grassland through a singular disturbance, such as a fire, or through a combination of incremental and sudden disturbances such as fire, climate change, and human agricultural practices.

Ecology reserach now seeks to understand the ways that changes in ecosystem states, often called regime shifts, occur when systems cross a “threshold” to a definitively new state (Ludwig et al. 1997; Scheffer et al. 2001; Beisner et al. 2003; Carpenter 2003; Scheffer & Carpenter 2003). Thresholds are definitive and irreversible, while transitions between states of an ecosystem may be either reversible or irreversible without human interventions (Stringham et al. 2003). State-and-transition models (STMs) in ecology identify thresholds and shifts between alternate states (domains of climate, soil, and vegetation) and are increasingly used to understand how ecosystems change from both human actions and natural events (Westoby et al. 1989). States are defined in relation to human management. Models use current and historical ecological data to characterize different possible states of a local site and determine factors that can move it between states (Westoby et al. 1989; Stringham et al. 2003).

Though ecological management recognizes the possibility of system shifts, water resource managers typically emphasize stable supplies and optimal outcomes. This is true in both theoretical literature and applied practice. Hashimoto et al (1982) first outlined definitions for resiliency, reliability, and vulnerability in a water system to assess alternative designs and operational performance. They defined: reliability as the probability, α, that a system is in a satisfactory operational state, with only two possible states, operation or failure; vulnerability as the likely magnitude of failure, when failure occurs; and resiliency as the likely system recovery time following failure, (drawing closely on stability). Resiliency, γ, is given by:


The reciprocal of resiliency in the equation above is the average recovery time from failure.

Yet, just within water resources, many more possible definitions exist.  Resilience metrics can be classified by two main approaches: 1) metrics that measure how far a system is from a critical threshold, and 2) metrics that measure changes in the possible landscape of potential system states over time (Fiering 1982a; Wang et al. 2009). The benefits and tradeoffs of how you assess such metrics varies in these conceptions reliability, resilience, and vulnerability, (Moy et al. 1986; Kundzewicz & Laski 1995; Vogel & Bolognese 1995; Vogel et al. 1999; Kjeldsen & Rosbjerg 2004; Wang et al. 2009). The majority of research and practices focus on single system components, or engineering resilience conceptions, which minimize deviations from expected outcomes. In the future, managers of environmental resources will need to consider alternative definitions of resilience.



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