Evaluation of dynamic coastal response to sea-level rise modifies inundation likelihood

Abstract

2 Sea-level rise (SLR) poses a range of threats to natural and built environments 1,2 , making assessments of SLR-induced hazards essential for informed decision-making 3 . We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30x30 m resolution predictions for more than 38,000 km 2 of diverse coastal landscape in the northeastern United States (U.S.). Probabilistic SLR projections, coastal elevation, and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response vs. inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge.This approach is well-suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints. As an alternative, we developed a data-driven coastal response (CR) model that considers both inundation and dynamic response using a range of SLR scenarios and datasets describing elevation and vertical land movement. We integrate these elements with land-cover information to assess CR likelihoods in the form of a dynamic probability, DP = 1-Prob. (inundate), using a Bayesian network Maine through Virginia, and includes a region with a wide range of coastal development, infrastructure, and environments found globally; including uplands, barrier beaches, spits, islands, mainland beaches, cliffs, rocky headlands, estuaries, and wetlands. The study area is defined by the -10 and +10 m elevation contours and mapped as a 30 m grid.To predict CR likelihoods (Figure 2), we first compute an adjusted land elevation with respect to projected sea levels:where AE represents the adjusted elevation with respect to a future sea level; E denotes the initial land elevation; SL is a projected sea level in the 2020s, 2030s, 2050s, or 2080s; and VLM gives the current rate of vertical land movement due to glacial isostatic adjustment, tectonics, and other non-climatic effects such as groundwater withdrawal and sediment compaction 15 . Sources of uncertainty in AE predictions include SLR projections, elevation data accuracy, vertical datum adjustments, and the interpolation of VLM rates from point data; these geospatially-explicit input uncertainties are propagated through the model to produce a probability mass function P(AE) for every grid cell (Figure 2c,d). Once generated, AEs are related through evaluation of their dynamic response potential with generalized landcover information and used to produce a CR likelihood (Figures 1, 2).Discretized AE predictions provide an estimated submergence level comparable to many existing inundation models 3, 16 (Figure 2). However, our predictions include several notable improvement...

Compounding effects of sea level rise and fluvial flooding

“…Without flood adaptation, annual losses of 0.3 to 9.3% of global gross domestic product are expected by 2100 (47), while only ∼70% of the coastal landscapes projected to experience future flooding have some capacity to respond dynamically to SLR (48). High-quality sea surface WL data, recorded at tide gauges around the world over the last 100+ y, document a significant globally averaged acceleration in mean SLR of about 0.009 mm·y −2 since 1880 and 0.022 ± 0.015 mm·y (54).…”

Effective inundation of continental United States communities with 21st century sea level rise

“…Our projections assume that future tidal ranges will not differ substantially from those during the reference period, though there is evidence that sea level rise may increase tidal range (Flick et al 2003;Passeri et al 2016). While the sea level rise projections we use incorporate local rates of vertical land movement, we do not model any changes in coastal morphology, although such changes are likely to occur as sea level rises (FitzGerald et al 2008;Lentz et al 2016).…”

Modeling the Economic Value of Blue Carbon in Delaware Estuary Wetlands: Historic Estimates and Future Projections

“…Marshes can accrete vertically in sediment rich estuaries, but fail to keep pace with sea level rise in sediment poor estuaries or when belowground biomass productivity is insufficient to increase soil volume (Lentz et al, 2016;Weston, 2014). Tides and wave action influence the seaward boundary of marshes (Mariotti and Fagherazzi, 2010), whereas surface hydrology and plant interactions determine the landward boundary with uplands.…”