Disaster Recovery Curve Analysis

Resilience loss is the cumulative shortfall in functionality over the recovery period — the area between the pre-event baseline and the recovery curve from the moment of impact until functionality returns. It integrates two things at once: how deep the initial drop was and how long the system stayed below normal. A deep but brief disruption and a shallow but prolonged one can have similar resilience losses. Expressing resilience this way, as the 'missing' functionality over time, gives a single comparable number that reflects the whole recovery, not just the size of the initial blow.

Loss estimation quantifies the immediate impact of a disaster — the damage and losses at or shortly after the event. Recovery curve analysis picks up where loss estimation ends, tracing how functionality returns over the months and years that follow and quantifying resilience from that trajectory. The initial drop on the recovery curve is essentially the output of a loss model; the curve then adds the recovery dynamics — speed, shape, completeness — that loss estimation does not address. The two are complementary stages of analyzing a disaster's consequences.

Yes. Recovery trajectories are not limited to returning to the pre-event baseline; a curve can overshoot it when reconstruction improves on prior conditions — the build-back-better outcome — or can fall short and stabilize below baseline when recovery is incomplete. The shape of the curve captures these possibilities, and the resilience metrics reflect them: a curve that rises above baseline indicates net improvement, while a stalled curve indicates persistent loss. This flexibility is part of why recovery curves are useful for evaluating resilient recovery.