Future changes in built environment risk to coastal flooding, permanent inundation and coastal erosion hazards Journal Paper
- Author
- Stephens, S.A.; Paulik, R.; Reeve, G.; Wadhwa, S.; Popovich, B.; Shand, T.; Haughey, R.
- Year
- 2021
- Journal / Source
- Journal of Marine Science and Engineering
- Volume
- 9
- Number
- 9
- Pages
- 20
- Summary
- Sea-level rise will cause erosion of land, deeper and increasingly frequent flooding and will eventually permanently inundate low-elevation land, forcing the adaptation of seaside communities to avoid or reduce risk. To inform adaptation planning, we quantified the effects of incremental relative sea-level rise (RSLR) on exposed land area, number and replacement value of buildings within Tauranga Harbour, New Zealand. The assessment compared three coastal hazards: flooding, permanent inundation and erosion. Increasingly frequent coastal flooding will be the dominant trigger for adaptation in Tauranga. In the absence of adaptation, coastal flooding, recurring at least once every 5 years on average, will overtake erosion as the dominant coastal hazard after about 0.15–0.2 m RSLR, which is likely to occur between the years 2038–2062 in New Zealand and will rapidly escalate in frequency and consequence thereafter. Coastal erosion will remain the dominant hazard for the relatively-few properties on high-elevation coastal cliffs. It will take 0.8 m more RSLR for permanent inundation to reach similar impact thresholds to coastal flooding, in terms of the number and value of buildings exposed. For buildings currently within the mapped 1% annual exceedance probability (AEP) zone, the flooding frequency will transition to 20% AEP within 2–3 decades depending on the RSLR rate, requiring prior adaptive action. We also compared the performance of simple static-planar versus complex dynamic models for assessing coastal flooding exposure. Use of the static-planar model could result in sea level thresholds being reached 15–45 years earlier than planned for in this case. This is compelling evidence to use dynamic models to support adaptation planning.