globalchange  > 气候变化事实与影响
DOI: doi:10.1038/nclimate2474
论文题名:
Barrier island bistability induced by biophysical interactions
作者: Orencio Durá; n Vinent
刊名: Nature Climate Change
ISSN: 1758-1062X
EISSN: 1758-7182
出版年: 2014-12-22
卷: Volume:5, 页码:Pages:158;162 (2015)
语种: 英语
英文关键词: Geomorphology
英文摘要:

Barrier islands represent about 10% of the worlds coastline1, sustain rich ecosystems, host valuable infrastructure and protect mainland coasts from storms. Future climate-change-induced increases in the intensity and frequency of major hurricanes2 and accelerations in sea-level rise3, 4 will have a significant impact on barrier islands5, 6—leading to increased coastal hazards and flooding—yet our understanding of island response to external drivers remains limited1, 7, 8. Here, we find that island response is intrinsically bistable and controlled by previously unrecognized dynamics: the competing, and quantifiable, effects of storm erosion, sea-level rise, and the aeolian and biological processes that enable and drive dune recovery. When the biophysical processes driving dune recovery dominate, islands tend to be high in elevation and vulnerability to storms is minimized. Alternatively, when the effects of storm erosion dominate, islands may become trapped in a perpetual state of low elevation and maximum vulnerability to storms, even under mild storm conditions. When sea-level rise dominates, islands become unstable and face possible disintegration. This quantification of barrier island dynamics is supported by data from the Virginia Barrier Islands, USA and provides a broader context for considering island response to climate change and the likelihood of potentially abrupt transitions in island state.

Barrier islands respond to rising sea level by migrating landward or drowning7, 9, 10. Landward migration is driven mostly by storms and is controlled by island elevation. Extensive measurements of dune elevation performed at the Virginia Barrier Islands11, a relatively undisturbed barrier system including 12 islands, show a bimodal distribution of barrier island elevation with two well-defined island types: low-elevation and high-elevation islands (Fig. 1 and Supplementary Fig. 1). Low islands lacking vegetated dunes are relatively narrow and prone to frequent overwash, resulting in rapid landward migration (Fig. 1a, b, g) and low biodiversity (as in the case of the islands associated with the Mississippi Delta, for example, the Chandeleur Islands). In contrast, high islands with well-developed dunes resist storm impacts, are wider and migrate slowly (if at all, Fig. 1c, d, g) and support a rich ecosystem and/or human development. In this way, barrier island evolution is fundamentally linked to dune dynamics. However, because vegetated dunes both protect islands from storm impacts and are themselves eroded by storms and affected by rising sea level, island dynamics ultimately arise from the competition between dune erosion and dune formation.

Figure 1: Empirical evidence for barrier island bistability.
Empirical evidence for barrier island bistability.

ad, Examples of low (a,b) and high (c,d) barrier islands, that is, islands without and with well-developed dunes, along the Virginia Barrier Islands, US mid-Atlantic coast (included map): Paramore (a), Myrtle (b) and Hog (c,d). e, Alongshore island elevation (elevation of primary dune relative to beach berm)11 derived from 2005 lidar data (solid line) and average island elevation (filled circles). f, Probability density function (PDF) of measured elevations (symbols) with best fit by a bimodal normal distribution (lines). The crossover elevation is used to define high (blue) and low (yellow) regions in e. g, Average island shoreline change rate (squares), used as a proxy for island migration, and island width (triangles), as function of average island elevation H. Shoreline change rates are calculated from shoreline positions in the period 1945–2005 (ref. 12) and island widths are calculated from reported data on island area13. h, PDF of simulated island elevations (circles) and simulated shoreline change rate (squares) rescaled by the value at the low state, as function of the average island elevation. Simulations were performed for γ = 1.5. i,j, Photos illustrate shell armouring (i) and the exposure of marsh platform on the foreshore (j) at the two low-elevation locations noted by corresponding symbols in the map.

The coastal dune model describes the temporal evolution of the sand surface elevation h (x, y, t)—defined relative to the MHWL—and the cover fraction ρveg (x, y, t) for a single generic grass species. x is the cross-shore distance to the shoreline (x = 0), which separates the foreshore (x < 0) from the backshore (x > 0), and y is the alongshore coordinate. A complete description of the coastal dune model is provided in the Supplementary Methods and includes further details on the calculation of the aeolian transport and vegetation dynamics as well as the initial and boundary conditions used to integrate the model.

Aeolian sand transport.

In the absence of storms, the sand flux qa(x, y, t) is calculated from the bed wind shear stress, which depends on the surrounding topography and the vegetation cover, and the local sand transport threshold, which we assume is primarily controlled by the sand moisture content. For simplicity in the formulation, sand transport is described by volume, not mass, flux.

Storms.

Storms are defined in the model as HWEs with total water elevation R above the MHWL (R is defined relative to the MHWL). HWEs are considered periodic with period THWE and have constant duration. R is randomly distributed following an Erlang distribution ( is mean total water elevation), which has an exponential tail, in agreement with ref. 28, and filters tidal events (that is, there are no events for R right arrow 0).

Storm-induced sand transport.

We derive a phenomenological expression to calculate the cross-shore sand flux qst(x, y, t) for elevations between MHWL and R during HWEs. On the basis of ref. 29 (see ref. 30 for the validation of a similar formulation), we assume the net sand flux qst over many swash cycles is proportional to the cube of the average speed U of the uprushing wavefront, times the time δt this particular location is submerged: qstU3δt/(gT), with gravity g and timescale T. The net sand flux is weighted by a downslope contribution (1 − xh/tan(α)) that represents the tendency of the surface to reach the equilibrium foreshore slope tan (α). From energy conservation, the kinetic energy of the uprushing wavefront is approximately balanced by its potential energy. In this case and (ref. 29), which leads to

We choose the rescaled duration of HWEs to qualitatively reproduce the main erosional regimes described in ref. 18 (Supplementary Fig. 4). Crucially, the predicted bistable behaviour of barrier islands depends only on the existence of these erosional regimes and not on the details of the sand flux formulation.

Surface dynamics.

At the foreshore (x < 0), we assume an equilibrium defined by a constant slope of angle α. This assumption implies that aeolian erosion is balanced by accretion in the swash zone. As a result, the simulated foreshore acts as a sand reservoir supplying an unlimited amount of sediment to the backshore, effectively feeding dune formation and post-storm recovery. For the shoreline (x = 0), we assume, as a first approximation, that under RSLR it follows Bruuns rule and migrates landward at a rate proportional to the rate S of RSLR. At the backshore (x > 0), we calculate the change in the sand surface elevation h from mass conservation as

during HWEs, and

otherwise. The last two terms at the right-hand side describe the effects of RSLR and appear because we define the surface elevation relative to the sea level and the shoreline position.

Vegetation dynamics.

As a first approximation, we assume a single generic grass species with a cover fraction ρveg that is sensitive to sand erosion and accretion and that can increase up to the maximum cover ρveg = 1 during a characteristic time tveg. We further assume plant growth is also sensitive to frequent saltwater inundation and thus to the proximity of the shoreline and to the elevation above MHWL, such that plants can effectively grow only landward of the vegetation limit Lveg and in places higher than a minimum elevation Zc. Thus,

where the Heaviside function Θ(s) (1 for s > 0; 0 otherwise) defines the regions where plants can grow.

Parameters.

We investigate model outcomes as function of the parameters characterizing the external forcing and the response of the system (the explored range is in parenthesis): the vertical vegetation limit Zc (0.02–0.2 m), frequency of HWEs THWE (0–20 yr), mean total water elevation (1–2 m), rate S of RSLR (0–0.02 m yr−1) and the imposed onshore wind, characterized by the ratio of the undisturbed shear velocity u0 and the transport threshold ut (1.5–2.5), and by the fraction rt of the time the wind is above the transport threshold and sand is available.

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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/4890
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Orencio Durá,n Vinent. Barrier island bistability induced by biophysical interactions[J]. Nature Climate Change,2014-12-22,Volume:5:Pages:158;162 (2015).
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