This article offers a literature-supported conception and empirically grounded analysis of surprise by exploring the capacity of scenario-driven, agent-based simulation models to better anticipate it. Building on literature-derived definitions and typologies of surprise, and using results from a modeled 81,000 ha study area in a wildland-urban interface of western Oregon’s Willamette Valley Ecoregion, the paper explores surprise by analyzing alternative future deviations from historical fire size at multiple spatial and temporal scales. It investigates whether, how and why modeled patterns and likelihoods of surprising fires in the next half-century differ under climate change from those of the past half-century.
Working from Holling’s (1986) definition of surprise, we use fire history records (1960–2011) to define expectations for future fire behavior (2007–2056) as evidenced through fire size and likelihood. Using geodesign techniques, we model alternative future fires under two future climate regimes, and contrast them with expectations derived from the fire history record to identify instances when fire size and likelihood deviate from expectations in surprising ways. Data science techniques are employed to explore and characterize the landscape’s alternative future trajectories in time:space envelopes that bound surprising fires. We argue that if the design and planning disciplines are to help society anticipate surprise,they must shift attention from primarily deterministic approaches to those that probabilistically explore trajectories from current to future landscapes. We conclude with general suggestions for how geodesign techniques and tools could be used to anticipate surprise in other landscapes, for phenomena other than fire.