Coupled Urban Change and Natural Hazard Consequence Model for Community Resilience Planning
Dylan R. Sanderson, Daniel T. Cox, Mehrshad Amini and 1 more
Earth's Future · 2022
doi:10.1029/2022ef003059 (opens in a new tab)
We email you when a new notice or a question about this paper appears.
Abstract
Abstract This paper presents a new coupled urban change and hazard consequence model that considers population growth, a changing built environment, natural hazard mitigation planning, and future acute hazards. Urban change is simulated as an agent‐based land market with six agent types and six land use types. Agents compete for parcels with successful bids leading to changes in both urban land use—affecting where agents are located—and structural properties of buildings—affecting the building's ability to resist damage to natural hazards. IN‐CORE, an open‐source community resilience model, is used to compute damages to the built environment. The coupled model operates under constraints imposed by planning policies defined at the start of a simulation. The model is applied to Seaside, Oregon, a coastal community in the North American Pacific Northwest subject to seismic‐tsunami hazards emanating from the Cascadia Subduction Zone. Ten planning scenarios are considered including caps on the number of vacation homes, relocating community assets, limiting new development, and mandatory seismic retrofits. By applying this coupled model to the testbed community, we show that: (a) placing a cap on the number of vacation homes results in more visitors in damaged buildings, (b) that mandatory seismic retrofits do not reduce the number of people in damaged buildings when considering population growth, (c) polices diverge beyond year 10 in the model, indicating that many policies take time to realize their implications, and (d) the most effective policies were those that incorporated elements of both urban planning and enforced building codes.
Abstract licence: CC BY-NC
Reads
Read by people on this page
None yet.
Checked or read by AI through naulon
None yet.
A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.
Are you an author?
Is this your paper?
Claim it to add your note and be paid when agents cite it. Free.