This study explores the uncertainty introduced in
global assessments of coastal flood exposure and risk when not accounting for
water-level attenuation due to land-surface characteristics. We implement a
range of plausible water-level attenuation values for characteristic
land-cover classes in the flood module of the Dynamic and Integrated
Vulnerability Assessment (DIVA) modelling framework and assess the
sensitivity of flood exposure and flood risk indicators to differences in
attenuation rates. Results show a reduction of up to 44 % in area
exposure and even larger reductions in population exposure and expected flood
damages when considering water-level attenuation. The reductions vary by
country, reflecting the differences in the physical characteristics of the
floodplain as well as in the spatial distribution of people and assets in
coastal regions. We find that uncertainties related to not accounting for
water attenuation in global assessments of flood risk are of similar
magnitude to the uncertainties related to the amount of sea-level rise
expected over the 21st century. Despite using simplified assumptions to
account for the process of water-level attenuation, which depends on numerous
factors and their complex interactions, our results strongly suggest that an
improved understanding and representation of the temporal and spatial
variation of water levels across floodplains is essential for future impact
modelling.
Introduction
Increased flooding due to sea-level rise (SLR) is a major natural hazard that
coastal regions will face in the 21st century, with potentially high
socio-economic impacts (Kron, 2013; Wong et al., 2014). Broad-scale (i.e.
continental to global) assessments of coastal flood exposure and risk are
therefore required to inform mitigation targets and adaptation decisions
(Ward et al., 2013a), related financial needs, and loss and damage estimates.
Towards these ends, a number of recent studies have assessed the exposure of
area, population and assets to coastal flooding at national to global scales
(Nicholls, 2004; Brown et al., 2016; Jongman et al., 2012a; Ward et al.,
2013b; Arkema et al., 2013; Muis et al., 2017) as well as flood risk (Hinkel
et al., 2014; Vousdoukas et al., 2018a).
Although methods for broad-scale coastal-flood exposure and risk assessment
vary between studies, flood extent and water depth have commonly been
assessed based on spatial analysis, assuming that all areas with an elevation
below a certain water level that are hydrologically connected to the sea are
flooded (the “bathtub” method) (Poulter and Halpin, 2008; Lichter et al.,
2011). Notable exceptions are the studies of Dasgupta et al. (2011), who used
a simple approach to account for wave height attenuation with distance from
the coast, and Vousdoukas et al. (2018b), who, for the Iberian Peninsula,
adopted a modified version of the bathtub approach that also considers water
volume. The use of simplified methods for assessing flooding is primarily
related to difficulties of using hydrodynamic methods at a broad scale,
namely the limited availability and large volume of the necessary
high-resolution input data and the prohibitive computational costs, which
render hydrodynamic modelling applications impractical at global scales
(Ramirez et al., 2016). Therefore, global applications have utilised
elevation data with a spatial resolution of 1 km and a vertical resolution
of 1 m (Mondal and Tatem, 2012; Jongman et al., 2012b; Ward et al., 2014),
with only a few recent studies employing higher spatial resolution (90 m)
datasets (e.g. Hinkel et al., 2014; Vousdoukas et al., 2018a; see also de
Moel et al., 2015).
Hydrodynamic models are normally used only for local-scale applications. This
is because they require detailed data on parameters such as coastal
topography, bathymetry and land use in order to represent local-scale processes
and to account for hydraulic properties. A range of simpler inundation models
that partly account for hydraulic processes at intermediate scales using
medium-resolution elevation data (<100 m2) have also been applied at
subnational scales (e.g. Bates et al., 2010; Wadey et al., 2012; Lewis et
al., 2013; Ramirez et al., 2016), and these models are beginning to inform
analysis at broader scales (e.g. Vousdoukas et al., 2016, 2018a). There is
also developing literature on hydrodynamic modelling of water-level
attenuation over coastal wetlands at the landscape scale (<1 km) for salt
marshes (Loder et al., 2009; Wamsley et al., 2009, 2010; Barbier et al.,
2013; Smith et al., 2016) and mangrove forests (McIvor et al., 2012; Zhang et
al., 2012; Liu et al., 2013). However, the incorporation of the above
processes in global models is still very limited.
Not accounting for hydrodynamic processes in global models can, however,
lead to overestimation of flood extent and water depth. Hydrodynamic models
capture processes that are not included in global models, such as the
effects of surface roughness (both natural and anthropogenic) and channel
network density and connectivity (and its effect on landscape continuity) on
the timing, duration and routing of floodwaters. For example, inundation
extent has been shown in some cases to significantly decrease in urban and
residential areas when the built environment is represented in numerical
simulations (e.g. tsunami inundation: Kaiser et al., 2011; storm surge
inundation: Brown et al., 2007; Orton et al., 2015).
To our knowledge, there is no study that has explored the uncertainty
introduced into global models by not accounting for water-level attenuation
due to hydrodynamic processes related to surface roughness. This paper aims
to address this gap. We derive a range of plausible water-level attenuation
values from existing literature and implement them in the flood module of
the Dynamic Interactive Vulnerability Assessment (DIVA) modelling framework
(Hinkel et al., 2014). Next, we assess the sensitivity of flood exposure and
flood risk indicators to plausible changes in water-level attenuation values
under a range of different SLR scenarios. Finally, we compare the
uncertainty due to water-level attenuation rates with the uncertainty range
associated with expected SLR during the 21st century.
Methods and dataThe Dynamic Interactive Vulnerability Assessment (DIVA) modelling
framework
DIVA is an integrated, global modelling framework for assessing the
biophysical and socio-economic consequences of SLR, and associated extreme
water levels, under different physical and socio-economic scenarios and
considering various adaptation strategies (Hinkel and Klein, 2009). DIVA has
been widely used for global- and continental-scale assessments of SLR
impacts, vulnerability and adaptation (e.g. McLeod et al., 2010; Hinkel et
al., 2010, 2013, 2014; Brown et al., 2016; Spencer et al., 2016; Schuerch et
al., 2018). It is underpinned by a global coastal database which divides the
world's coastline (excluding Antarctica) into 12 148 coastal segments
(Vafeidis et al., 2008). Each segment contains approximately 100 elements of
data concerning the physical, ecological and socio-economic characteristics
of the coast. Here we focus on the impacts of increased exposure to coastal
flooding and the potential damage of extreme sea level events (due to the
combination of storm surges and astronomical high tides). We used the flood
module of DIVA (for details see Hinkel et al., 2014) to estimate potential
coastal flood damage, SLR impacts and associated costs.
We specifically considered the following five indicators, which
progressively include additional components of flood risk:
Area below the 1-in-100-year flood event (km2), an estimate based on
elevation data and information on water levels for a single hazard event
(i.e. the height of the 1-in-100-year sea flood);
People living in the 1-in-100-year floodplain, a calculation based on
spatial data on elevation and population as well as on information for a
single hazard event (i.e. the height of the 1-in-100-year sea flood);
Assets in the 1-in-100-year floodplain (USD), a calculation that uses
data on elevation, population, gross domestic product (GDP) and information
for a single hazard event (i.e. the height of the 1-in-100-year sea flood);
Expected value of the number of people flooded per year (hereafter, people
flooded), a calculation based on elevation and population data and the
probability distribution of the hazard (i.e. sea flood heights and their
probability of occurrence); and
Expected value of annual damages to assets (hereafter, flood damage) (USD), a calculation based on elevation, population, GDP data and the
probability distribution of the hazard (i.e. sea flood heights and their
probability of occurrence).
For each coastline segment, a cumulative exposure function for area and
population that gives the areal extent (hydrologically connected to the sea)
and number of people below a given elevation was constructed. Damages to
assets were assessed using a depth-damage function with a declining slope,
with 50 % of the assets being destroyed at a water depth of 1 m
(Messner et al., 2007).
Coastal elevation and rate of water-level attenuation
To simulate the effect of different values of attenuation at the broad scale,
we implemented a stylised elevation profile to represent the process of
water-level attenuation. We assumed that water levels decrease at a constant
slope (α) with increasing distance from the coastline.
Location-specific coastal profiles for every coastline segment were based on
floodplain areas contained within the DIVA database. The database reports
total land area within different elevation increments (<1.5, 1.5–2.5,
2.5–3.5, 3.5–4.5, 4.5–5.5, 5.5–8.5, 8.5–12.5, 12.5–16.5 m) for each
coastal segment. The elevation dataset that was used for estimating
floodplain areas and developing the segment elevation profiles is the
commonly used Shuttle Radar Terrain Mission (SRTM) digital elevation database
(Jarvis et al., 2008), which has a vertical resolution of 1m and a spatial
resolution of 3 arcsec (∼90 m at the equator).
We approximated the average coastal profile for every segment by assuming
that elevation continuously increases with distance from the shore. Starting
with the lowest elevation increment, the floodplain areas of all elevation
increments were cumulatively summed to retrieve the total area below a
certain elevation. The total areas were then divided by the segment length
to derive the inundation length of the respective floodplain (dxi). To
evaluate the representativeness of the assumption of continuously increasing
elevation with increasing distance from the shore, we used the original SRTM
dataset and calculated the Euclidian distance of each cell to the nearest
coastline for every pixel. Mean distances from the coast were calculated for
each of the floodplain areas of each segment. Subsequently, we compared
these mean distances with the respective average floodplain elevation for
each DIVA coastline segment to analyse the validity of the
“continuous-increase” assumption. This comparison revealed that 55 % of
the DIVA coastline segments show either a continuous increase or no change
in the mean distance along the elevation profile (Fig. 1a), suggesting
that elevation does not decrease with distance from the coast. Comparing all
elevation increments of all segments (i.e. pairwise comparison of the mean
distances of consecutive elevation increments in a segment), there was an
increase, or no change, in the mean distance from the coastline in 88 % of
cases. Only 12 % of cases showed a decrease (Fig. 1b). This result
indicates that the stylised continuous profile (Fig. 1a) can be regarded
as representative of global coastal topography (see also Schuerch et al.,
2018).
Stylised coastal profile with (a) continuous and
(b) discontinuous increases in elevation with distance from the
shore.
We then adjusted the coastal profile using a range of possible attenuation
rates that represent different water surface slopes. Depending on the
applied value for water-level attenuation, the slope (α) of the
inundating water surface was employed to modify (incline) the coastal
profile. Based on this slope, the coastal profile is thereby elevated by the
amount of the water-level reduction (hxi) computed at a distance
dxi (Fig. 1):
hxi=tan(α)×dxi.
In this way the original floodplain areas and inundation depths are reduced
in order to account for the reduced (i) inundation length (dx) and (ii) inundation depth (hx) (see Fig. 2).
The stylised coastal profile, based on the floodplain areas in the
DIVA database (lower line), for two characteristic coastline segments (A with
a flat and B with a steep profile). Water-level attenuation is accounted for
by inclining the coastal profile according to Eq. (1) (upper line). Red dots
on the adjusted coastal profile indicate the inundation length in the case of
a water level with a constant slope of α, which represents the
attenuation rate and for an incident water-level equal to the corresponding
increment height.
Water-level reduction rates, for different types of land cover, as
reported in the literature.
Event typeLand cover typeLocationRate of water-level reductionMethodSourceStorm surgeBare land and marshModelled plat- form +0.5 m above sea level10 cm km-1 (no vegetation, no channels), 26 cm km-1 (100 % vegetation cover, no channels), 8 cm km-1 (100 % vegetation cover, channel network)Numerical modellingTemmerman et al. (2012)Hurricane Isaac (2012)MarshLouisianaUp to 70 cm km-1 water-level re- duction in presence of vegetation; 37 % reduction of total inundation volumeNumerical modellingHu et al. (2015)HurricanesMarshMultiple1 m per 14.5 km, 6.9 cm km-1 (range from 1 m per 5 km to 1 m per 60 km, 20–1.7 cm km-1)Field studyCorps of Engineers (1963) – in Wamsley et al. (2010)Hurricane Andrew (1992)MarshLouisiana1 m per 20–23.5 km, 5–4.3 cm km-1Field studyLovelace (1994)Hurricane Rita (2005)Louisiana1 m per 4 km to 1 m per 25 km, 25–4 cm km-1Field studyMcGee et al. (2006) in Wamsley et al. (2010)Hurricanes Wilma (2005) and Charley (2004)Mangroves and marshFlorida9.4–4.2 cm km-1Field studyKrauss et al. (2009)HurricanesMangrovesLouisiana23.3–1.7 cm km-1Field studiesMcIvor et al. (2012) (from various studies)Hurricane Wilma (2005)MangrovesSouth FloridaUp to 50 cm km-1 (6–10 cm km-1 in the absence of mangroves)Field study & modellingZhang et al. (2012)HurricanesMangrovesSouth Florida7.7–5.0 cm km-1ModellingLiu et al. (2013)
For the sensitivity analysis we used a range of attenuation rates that
embraces the values reported in the literature (Table 1), where the water
level under storm conditions has been shown to decrease with distance from
the coast. For reviewing the literature we employed the ISI Web of Knowledge
and based our search on the keywords “surge”, “attenuation” and
“water-level”. We selected studies that directly reported values of
water-level reduction with distance and did not include studies focussing on
wave attenuation. We must note that the aim was not to conduct a systematic
literature review but rather to identify a characteristic range of values
that could support the sensitivity analysis. The identified studies all
relate to coastal wetland environments. Although there are published studies
of localised water-level dynamics from flow–form interactions in urban and
other settings, we have not come across similar landscape-scale assessments
for other land use types. Therefore we broadened this review, where reported
attenuation values were up to 70 cm km-1, by directly contacting
scientists and data analysts with experience in field or modelling studies.
Following their expert judgement, we extended our analysis to include
attenuation rates of up to 100 cm km-1 as an upper limit.
We further constrained the sensitivity analysis by adjusting the range of
water attenuation rates for each segment based on the predominant land use
type covering the area of every elevation increment. For estimating the
predominant land use we employed the GlobCover Land Cover V2.3 dataset, a
global land cover dataset with a resolution of 10 arcsec
(∼300 m at the equator). It is based on the ENVISAT
satellite mission's MERIS sensor (Medium Resolution Image Spectrometer)
covering the period between January and December 2009 and includes 22 land
cover classes. As the available information on water attenuation rates by
land use type is limited, we reclassified the data to seven classes (forest,
urban, cropland, grassland, mangroves, salt marshes and unknown) and assigned
maximum attenuation rates to each class (Table 2). For the model runs we
used the five attenuation categories (no, low, medium, high and maximum
attenuation) corresponding to 0, 25 %, 50 %, 75 % and 100 % of the
maximum values found in the literature or from expert judgement, for each
class. These rates were then used to incline the water surface in order to
represent a constant water-level attenuation and the associated reduction in
water levels (α) across the floodplain for each coastline segment.
Maximum attenuation rates per land use class used in the sensitivity
analysis.
Land use classMaximum attenuation(cm km-1)Forest (1)50Urban (2)100Cropland (3)40Grassland (4)25Mangroves (5)50Salt marshes (6)25Unknown (0)25Sea-level rise and socio-economic scenarios
For global SLR in 2100 from a 1985–2005 baseline we used three scenarios:
the 5 % quantile of the low Representative Concentration Pathway (RCP)
2.6, the median of the medium scenario RCP 4.5 and the 95 % quantile of
the high scenario RCP 8.5. These scenarios are represented by regionalised
SLR projections, with a global mean rise of 29, 50 and 110 cm (by 2100 with
respect to 1986–2005), respectively, and were developed in the
Inter-Sectoral Impact Model Intercomparison Project Fast Track (for full
details see Hinkel et al., 2014). Following Menendez and Woodworth (2010),
once mean sea level had been determined, future extreme water levels were
obtained by displacing upwards extreme water levels for different return
periods (as included in the DIVA database) with the rising sea level.
We used a single shared socio-economic pathway (SSP), namely SSP2, to
represent changes in coastal population and assets. SSP2 reflects a world
with medium assumptions between the other four SSPs, in terms of resource
intensity and fuel dependency as well as GDP and population development
(O'Neill et al., 2014). Finally, we ran the DIVA model using a no-dike
scenario, where no defence measures for preventing coastal flooding are
present. This was done to better characterise water attenuation and to
reduce complexity as dike heights in DIVA are modelled since no consistent
global data on coastal protection exist (Schuerch et al., 2018).
Results
We present results for the different classes of attenuation rates, across
the five indicators that progressively include additional components of
flood risk.
Reduction of current flood exposure and risk
Table 3 shows the results from the five categories of attenuation rates and
both the absolute and percentage reductions in the values of the five
indicators against this baseline.
Reduction, relative to the bathtub method, of five indicators of
global exposure and risk for different water-level attenuation rates. Values
are for a medium SLR scenario, in 2015.
Water-level attenuation category NoLowMediumHighFull(% decrease)(% decrease)(% decrease)(% decrease)Area below the 1-in-100-year727 714556 677488 183444 100410 873flood (km2)(23 %)(33 %)(39 %)(44 %)Number of people below the 1-in-100-174113968781year flood (million)(35 %)(45 %)(50 %)(53 %)Assets below the 1-in-100-year10 0736646554149564566flood (billion USD)(34 %)(45 %)(51 %)(55 %)Number of people flooded2.741.721.491.321.22(millions per year)(37 %)(46 %)(52 %)(55 %)Flood damages to assets for the 1-in-100-434304237233211year flood (billion USD per year)(30 %)(45 %)(46 %)(51 %)
Our results show that accounting for water-level attenuation in the
assessment of flooding results in large differences in the values of the five
indicators. For example, the area exposed to the 1-in-100-year flood in 2015
decreases by up to 44 % with the application of attenuation rates. The
low attenuation category results in an area reduction of 23 % while the
use of medium attenuation rates results in a reduction of 33 % (see
Table 3). Interestingly, the number of people in the 1-in-100-year floodplain
reduces to 87 million when considering high attenuation. This is a reduction
of 50 %, which is similar to the respective reduction in assets
(51 %) but higher than the reduction in area (44 %) exposure. This
result reflects the high population density near the coast that has been
reported in previous studies (e.g. Neumann et al., 2015). Flood damages from
the 1-in-100-year event are reduced by a similar proportion, totalling a
reduction of more than USD 220 billion (54 %) globally, when
considering maximum attenuation rates.
Relative reduction in area exposure to 1-in-100-year coastal floods
for low-attenuation (25 %) and high- attenuation (75 %) categories for 2020.
Absolute and relative reduction of the 1-in-100-year floodplain area
and associated exposed assets when applying different water-level attenuation
rates for Bangladesh, China and the USA in 2015. Values assume a medium SLR
scenario.
The reduction in impacts is not uniform across the globe and varies
considerably between different countries. Some examples are given in Fig. 3 and Table 4. Figure 3 shows the spatial variability of the effects of
accounting for water attenuation: low water attenuation can lead to
reductions in area exposure of more than 50 % and high attenuation can
reduce area exposure by more than 80 %. Table 4 shows results for three
countries, namely China, Bangladesh and the USA, where accounting for water-level attenuation reduces area exposure by up to 73 % in China, 39 % in
Bangladesh and 49 % in the USA. At the same time, the reduction in annual
flood costs follows a different trend, with exposed assets reducing by up to
75 % in China, 41 % in Bangladesh and 36 % in the USA, reflecting
differences in the elevation distribution and land cover characteristics of
the floodplains, as well as in the spatial distribution of people and assets
in the coastal regions of these countries.
Comparison of attenuation rate uncertainty with sea-level rise
uncertainty
Figure 4 illustrates the area of land located below the 1-in-100-year storm
surge level (H100), plotted against the different attenuation rates for
water-level change. The inclusion of water-level attenuation in the
assessment of flooding results in large reduction in the extent of the
100-year floodplain in 2100 (Fig. 4) under all SLR scenarios. Even the use
of low attenuation of water levels results in a reduction of 230 000 km2 of area exposed to the 1-in-100-year flood under the no-SLR
scenario. This increases to 350 000 km2 under the high-SLR scenario.
For the medium-SLR scenario (median of the medium scenario RCP 4.5; 50 cm by
2100), this reduction amounts to 31 % and 40 % of the total exposed area
at medium and full water-level attenuation respectively. The relative
reduction is larger (up to 60 %) for the high-SLR scenario compared to the
medium-, low- and no-SLR scenarios. Importantly, the overall difference in
the extent of the area of the 100-year floodplain between the no- and
high-SLR scenarios is of a similar order of magnitude to the difference in
area extent between the no-water and low-water-level attenuation rates, under any
scenario. This indicates that when assessing area exposure accounting for
even relatively moderate rates of water-level attenuation can be of similar
importance to the differences that result from different scenarios of SLR.
This analysis, therefore, strongly suggests that uncertainties related to
the omission of this factor in global assessments of flood risk are of
similar magnitude to the uncertainties related to the magnitude of SLR
expected over the 21st century.
Global total extent of the one-in-100-year floodplain, for different water-level attenuation rates and SLR scenarios.
Similar patterns can be observed for the exposure of population to the
1-in-100-year flood (Fig. 5). Low attenuation (Table 1) leads to a
reduction of more than 30 % in the exposure of population in 2100, under
the high-SLR scenario, bringing the number of people at risk in the 100-year
floodplain down by approximately 75 million. Moreover, medium attenuation
leads to a reduction in flood exposure by 100 million people, making
population exposure lower than the exposure under no SLR when attenuation is
not considered. Again, this result suggests that accounting for water-level
attenuation may be equally important to accounting for SLR uncertainty when
assessing the exposure of people to coastal flooding due to SLR.
Global estimates of population in the one-in-100-year floodplain for
different water-level reduction rates (Table 1) and SLR scenarios.
The value of assets exposed to the 1-in-100-year flood is also substantially
reduced, under all scenarios, when accounting for water-level attenuation
(Fig. 6). Considering low attenuation rates results in a decrease in the
exposure of assets of approximately 34 % in 2100, for a medium SLR
scenario. A reduction of 50 % in assets' exposure occur when high
attenuation is used. Furthermore, our results suggest that the use of a
relatively moderate attenuation rate has an interesting temporal dimension
as it shifts the extent of assets' exposure by approximately 30 years, under
all SLR scenarios (Fig. 6).
Temporal evolution of the amount of assets that are located in the
one-in-100-year floodplain for different water-level reduction rates (Table 1) and
SLR scenarios.
Damages also reduce considerably with the introduction of water-level
attenuation rates (Fig. 7). For example, the use of a low attenuation rate
results in a 34 % reduction in damages to assets in 2100 from the 1-in-100-year flood. The larger decrease in damages due to water-level attenuation
compared to population and area exposure is due to the fact that, besides
the decrease in the flood area extent, water-level attenuation leads to an
additional reduction of flood depth with distance from the coast. As water
depth is an important parameter for calculating damages to assets (Thieken
et al., 2005; Penning-Rowsell et al., 2013), depth reduction further reduces
the potential damages of assets due to flooding and results in a temporal
shift of damages of more than 25 years.
Comparison of the temporal evolution of sea-flood-damage estimates
for low, medium and high attenuation rates for different SLR scenarios.
Discussion and conclusions
This study highlights the importance of accounting for the effects of
hydrodynamic processes when assessing the impacts of coastal flooding at
national to global scales. In particular, water-level attenuation from the
interaction of extreme inundation events with vegetated surfaces can lead to
considerably lower estimates of exposure of land area and population to
coastal flooding. Furthermore, this effect can lead to large reductions in
potential damages, as lower water depths combined with smaller flood extents
give significantly lower flood-damage costs. The reduction in exposure and
risk is very pronounced, even when considering low water-level attenuation
rates.
Accounting for water-level attenuation appears to be as important in
assessing impacts as accounting for uncertainties related to the total
magnitude of SLR. In many of the cases explored, the difference in impacts
between no- and high-SLR scenarios is similar to the difference in impacts
between no and low attenuation rates of up to 12.5 cm km-1 (excluding urban
land use). This finding is of particular relevance in environments where the
floodplain substantially extends inland, such as in many of the world's
deltas and coastal plains.
It is widely acknowledged that the use of simplified methods, such as the
bathtub method, can provide useful first-order estimates of global impacts of
SLR and associated flooding (Lichter et al., 2011; Hinkel et al., 2014),
although an overestimation of flood extent and depth with the use of the
bathtub method should be generally anticipated (Vousdoukas et al., 2016).
Further, we must note that the reduction that we observe with the use of
water-level attenuation rates does not necessarily reflect actual impacts.
These are likely to depend on additional factors, which are usually not
considered in global assessments. For example, damage to assets in our
analysis is based solely on water depth; factors such as high local flow
velocities from channelised flow, storm wave impacts, inundation by saline
water and sedimentation from flood waters are not taken into account. Such
contributory factors can lead to an increased cost of damages and thus
counteract the lower impacts predicted from the use of a water-level
attenuation term alone. Furthermore, the analysis reported here is predicated
on the assumption of a continuous increase in elevation with increasing
distance from the shore. This study shows that whilst this assumption is
valid for the majority of coastal segments, there are segments where this
assumption does not hold true. In these cases model outputs may poorly
describe flood areas, flooded population numbers and asset damages and
incorrectly predict the effect of changes in the rate of water-level
attenuation. New improved versions of the SRTM elevation model (Yamazaki et
al., 2017) may help to partly address this limitation, while the lack of
open-access elevation data of higher accuracy and resolution still
constitutes a significant limitation for global studies (Schumann and Bates,
2018). Nevertheless, and despite these caveats, our results emphasise the
importance of accounting for uncertainties in impact assessments stemming
from the lack of consideration of water-level attenuation over coastal
plains.
Our approach means to provide an illustration of the potential effects of
water-level attenuation, as this process is not constant throughout the
floodplain and depends on numerous parameters beyond the type of the surface
cover. These factors include storm duration, wind direction, water depth and
vegetation traits (Resio and Westerink, 2008; Smith et al., 2016; Stark et
al., 2016). Furthermore, applying a constant slope to account for water-level attenuation is a strong simplification, since this will vary between
different storm events, but also under the influence of SLR. Nevertheless,
given the very high sensitivity of the outputs to even small changes in
water-level reduction rates, and the obvious lack of sufficient data on the
actual effect of different types of surface on attenuating water levels
during surges, we suggest that future work needs to focus on quantifying the
water-level attenuation terms for different land uses. Thus, for example,
both Brown et al. (2007), in the case of modelled flooding following storm-surge-induced sea defence failure, and Kaiser et al. (2011), in the case of
modelled tsunami wave impacts, have shown that disregarding buildings and
associated infrastructure (roads, gardens, ditches) when assessing
inundation can lead to a large overestimation of the extent of flooding.
Furthermore, given the large range of uncertainty with respect to the actual
values of water-level reduction associated with just one surface cover,
wetland habitat (Table 1), future impact modelling needs to focus on a
better understanding of the temporal and spatial variation of water levels
across floodplains that show a wide variety of land use types and human
occupancy, including densely urbanised regions (e.g. Lewis et al., 2013;
Blumberg et al., 2015).
Given that coastal wetlands can efficiently attenuate surge water levels,
the results of this study give a first estimate of how much of an impact
reduction may result from the implementation of large-scale, ecosystem-based
flood risk reduction management schemes (e.g. Temmerman et al., 2013). In
addition, achieving lower water levels through the establishment of coastal
wetlands not only reduces impacts but may also affect the timing of
potential adaptation tipping points by extending the anticipated lifetime of
adaptation measures. This would allow the development of alternative
adaptation pathways, a sequential series of linked adaptation options
triggered by changes in external conditions (Barbier, 2015), for coastal
regions.
Data availability
Data are made available in the main tables. Additional data
that support the findings of this study are available upon reasonable
request.
Author contributions
ATV and MS designed the research, with support from TS. MS extended the code
for the simulations. MS, CW, JLM prepared the data and conducted the
simulations, with support from DL. CW, JLM and ATV analysed the results. ATV
prepared the manuscript with contributions from TS, MS, CW, JLM, JH, DL, SB
and RJN. All authors discussed, reviewed and edited the different versions of
the manuscript.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “Global- and
continental-scale risk assessment for natural hazards: methods and
practice”. It is a result of the European Geosciences Union General Assembly
2018, Vienna, Austria, 8–13 April 2018.
Acknowledgements
Athanasios T. Vafeidis, Jan L. Merkens, Jochen Hinkel, Daniel Lincke,
Sally Brown and Robert J. Nicholls received funding from the European Union's
Seventh Framework Programme for Research, Technological Development and
Demonstration under grant agreement no. 603396 (RISES-AM project). This work
is a contribution (Tom Spencer) to “Physical and biological dynamic coastal
processes and their role in coastal recovery” (BLUECoast), UKRI NERC
(NE/N015878/1).
Review statement
This paper was edited by Hessel Winsemius and reviewed by two anonymous referees.
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