There is now a wealth of data to calculate global flood exposure. Available
datasets differ in detail and representation of both global population
distribution and global flood hazard. Previous studies of global flood risk
have used datasets interchangeably without addressing the impacts using
different datasets could have on exposure estimates. By calculating flood
exposure to different sized rivers using a model-independent
geomorphological river flood susceptibility map (RFSM), we show that limits
placed on the size of river represented in global flood models result in
global flood exposure estimates that differ by more than a factor of 2.
The choice of population dataset is found to be equally important and can
have enormous impacts on national flood exposure estimates. Up-to-date, high-resolution population data are vital for accurately representing exposure to
smaller rivers and will be key in improving the global flood risk picture.
Our results inform the appropriate application of these datasets and where
further development and research are needed.
Introduction
River floods are amongst the most frequent and damaging natural disasters
globally (Wallemacq et al., 2015). Considerable effort has gone into
understanding global river flooding over the last decade, and a number of
global flood models (GFMs) have been developed concurrently (Yamazaki et
al., 2011; Pappenberger et al., 2012; Winsemius et al., 2013; Rudari et al.,
2015; Sampson et al., 2015; Dottori et al., 2016c). The usefulness of these
GFMs was initially limited to coarse-scale flood risk assessments (Ward
et al., 2015) largely due to global-scale data limitations. However, the
incorporation of higher accuracy terrain data, available at the national
level, has shown that their modeling frameworks are also suited to
identifying more localized risk when utilizing local data (Wing et al.,
2017). Previous studies comparing GFMs have shown there is disagreement
between the global flood extents (Trigg et al., 2016b; Bernhofen et al.,
2018b; Aerts et al., 2020). This disagreement between GFMs stems from
different model structures and methods. One key difference between the
models, which has not yet been explored, is the size of their river
networks. The models have different river size thresholds at which they
simulate fluvial events. These thresholds determine the size, and number, of
rivers represented in GFMs, which can differ by several orders of magnitude.
The size of a model's river network is contingent on both the quality and
resolution of the model input datasets such as the underlying digital
elevation model (DEM) and climatology (Dottori et al., 2016c), as well as
the computational efficiency of the model, as the introduction of smaller
rivers exponentially increases the modeled domain. Chosen thresholds also
influence estimates of global flood exposure as larger river networks
result in higher simulated flood volumes and potential exposure. The effect
that GFM river network size has on flood exposure estimates has not yet been
quantified at the global scale. As remote sensing (RS) technologies continue
to advance, so will the granularity at which rivers can be represented
globally. Smaller rivers, previously unrepresented in coarse global
datasets, will be able to be studied and modeled at large scales,
potentially reframing current global flood exposure estimates. Limited work
has been dedicated to the investigation of the human interaction with rivers
of different sizes (Kummu et al., 2011).
Understanding this interaction globally, particularly with respect to river
flooding, will inform us about the completeness of current global flood
exposure studies and identify where further study and development are needed.
A comprehensive understanding of flood risk requires information about the
hazard, what or who is exposed, and their vulnerability. Exposure could
include damages (both direct and indirect), exposed gross domestic product
(GDP), exposed assets, and, most commonly, exposed people
(Ward et al., 2020).
Identifying flood-exposed populations usually involves intersecting a flood
hazard map with a population map. The methods and inputs used to produce
population datasets differ and so does their intended use
(Leyk et al., 2019). Recently released
population maps, which utilize commercial RS data and are an order of
magnitude more resolved than existing population datasets (Tiecke,
2017), are already being used for disaster preparedness and response
(Facebook, 2021). However, our current understanding of global flood
exposure is based on existing global population datasets, and these datasets
have been used interchangeably in global studies (Tanoue et al., 2016;
Jongman et al., 2012; Dottori et al., 2018) with little comment about their
relative merit. The credibility of existing global flood exposure estimates
in light of new, more detailed, population data and the implications of
their interchangeable use in studies of global flood exposure need to be
explored. A recent study by Smith et al. (2019) reported large
disagreement between flood exposure estimates calculated in 18 developing
countries using three different population datasets. The identification of
population data as one of the chief sources of uncertainty in global flood
exposure studies warrants further investigation at the global scale.
Understanding how both new and existing population datasets differ in their
resulting exposure estimates, both regionally and within the hierarchy of
the river network, can inform users about the most appropriate population
dataset to use.
To explicitly explore the impact of river network size on global flood
exposure estimates, we use a geomorphological measure of a river's flood
susceptibility, which is independent from current GFMs and the additional
uncertainties their different model structures bring. Fluvial processes
contribute to the evolution of a landscape over time. The erosional action
of flowing water has shaped the terrain of drainage basins to reflect the
historical flow of water through them. Geomorphological approaches to
mapping river flood susceptibility rely on the concept that the cumulative
hydrogeomorphic effect of past flood events, evident in topography data, is
indicative of a river's propensity to flood. Such approaches to flood
mapping have been applied over a number of scales: from local (Nardi et
al., 2006; Nobre et al., 2016; Dodov and Foufoula-Georgiou, 2006), to
national (Jafarzadegan et al., 2018; Samela et al., 2017), to regional
(Lugeri et al., 2010) and global (Nardi
et al., 2019). The computational efficiency of geomorphic flood mapping,
coupled with its reliance on only terrain data as input, make it useful for
a “first look” global scale analysis, intended to inform future development
of higher-accuracy hydrological flood mapping (Di
Baldassarre et al., 2020).
Our geomorphological approach to mapping a river's flood susceptibility,
herein referred to as the river flood susceptibility map (RFSM), is based on
new topography data (Yamazaki et al., 2017) which incorporate
crowdsourced information to better represent the locations of rivers and
streams (Yamazaki et al., 2019). Validation of our
calibrated methodology (outlined in detail in the Supplement)
shows that the RFSM better replicates GFM hazard maps in Africa than an
existing global geomorphological approach (Nardi et
al., 2019). We also show that the RFSM performs similarly to the best GFMs
(Dottori et al., 2016c; Sampson et al., 2015; Yamazaki et al., 2011) when
validated against historical flood events
(Bernhofen et al., 2018b). The RFSM allows us
to easily discretize the flood map into different river sizes (independently
of GFMs). We investigate the human interface with these different sized
rivers using three population datasets. Facebook's High Resolution
Settlement Layer (HRSL)
(https://data.humdata.org/organization/facebook?q=density, last access: 14 September 2021)
(1 arcsec, ∼30 m resolution at the Equator)
(Tiecke, 2017), which is currently only available in 168 countries
globally, and two population datasets used extensively in previous studies
of global flood risk: the Global Human Settlement Population (GHS-POP)
(10.2905/0C6B9751-A71F-4062-830B-43C9F432370F) (9 arcsec,
∼250 m resolution at the Equator) (Freire
et al., 2015; Schiavina et al., 2019) and WorldPop (10.5258/SOTON/WP00645) (3 arcsec, ∼90 m resolution at the Equator) (Stevens et
al., 2015; Lloyd et al., 2019). We present a global picture of flood
exposure to different sized rivers, both in the present day and how it has
changed over the past 40 years. We then compare the flood exposure
calculated using different population layers, exploring the implications
this has on national-level flood exposure estimates and examine the impact
that river size has on any disagreement. Finally, we address the size of
rivers represented in GFMs specifically and investigate how their chosen
river network size impacts both global and national flood exposure estimates
and what implications this has for previously published global flood risk
assessments.
MethodsMapping river flood susceptibility
We use a geomorphological approach to mapping river flood susceptibility,
which is independent from the global flood models (GFMs). Previous GFM
comparison studies found that multiple aspects of model structure
contributed towards disagreement (Trigg et al., 2016b; Bernhofen et al.,
2018b; Aerts et al., 2020). Using a geomorphological approach, we are able
to explore just one aspect of disagreement: river network size. This
approach allows us to explore all stream scales as drainage paths can be
identified from the terrain alone. It is not influenced by the structure of
the different GFMs and does not have the same computational restraints as a
global hydrodynamic model. This approach is different from the GFMs in that
it does not measure the flood extent for a given return period flood but
rather a river and surrounding location's static susceptibility to flooding.
There are different approaches to geomorphic floodplain mapping. Three
approaches were compared on the Tiber River in Central Italy by
Manfreda et al. (2014). That study found that approaches
utilizing morphological descriptors to delineate floodplains better
replicate reference flood extents. The best morphological descriptor was
found to be the relative elevation difference to the nearest channel (H). In
a follow up study, Samela et al. (2017) investigated 11 different morphological descriptors in the Ohio River basin and then tested
the best performing descriptors across the conterminous United States. While
H was amongst the best four descriptors, it was shown to be highly variable
across basins. The study found that the best morphological descriptor was a
geomorphic index which relates H to a function of the nearest channel's
contributing area. The method we use for delineating a river's flood
susceptibility is based on the height above nearest drainage (HAND)
methodology developed by Nobre et al. (2011). We use a
variable H value (Hn), which changes depending on the Strahler stream
order (Strahler, 1957) of the flooded channel (where n is the
Strahler stream order). This geomorphic approach, requiring only terrain
data as input, is computationally efficient and can be easily modified to
produce auxiliary data layers.
Our method, referred to as the river flood susceptibility map (RFSM)
(Bernhofen et al., 2021), is illustrated in Fig. 1 and takes three
gridded datasets as input: a digital elevation model (DEM), its derived
drainage directions, and its upstream drainage area (UDA). We use MERIT
Hydro data (Yamazaki et al., 2019), a hydrography
dataset based on the error-improved SRTM (Shuttle Radar and
Topography Mission) DEM: MERIT DEM (Yamazaki et al.,
2017). MERIT Hydro is an improvement on previously available global
hydrography datasets such as HYDROSHEDs (Lehner et al., 2008) in
terms of both spatial coverage and its representation of small streams. Its
improved representation of small streams is enabled by its incorporation of
global water body data and crowdsourced OpenStreetMap river data. This
makes it particularly suited to this study; we are interested in
examining the flood susceptibility of rivers down to the smallest streams.
Illustrative example of the method for deriving the river flood
susceptibility map (RFSM). (a) User-defined input parameters include the
minimum river size and the maximum relative elevation difference to the
nearest draining channel, Hn, for each Strahler stream order. Dataset
inputs include a digital elevation model (DEM), flow direction grid, and an
upstream drainage area grid (represented on a 12×12km2 grid for
illustrative purposes). Rivers (as defined by the minimum river size
threshold) are classified into Strahler stream orders. (b) Each Strahler
stream order is processed separately using the height above nearest drainage
(HAND) method, and then the layers are combined. In areas of overlap the
values for the highest-order streams are retained. (c) Two outputs are
produced: a map of the drainage area of the nearest flooded river and a map
of the Strahler order of the nearest flooded river. See Fig. 7 for an example of
RFSM outputs in Bosnia and Herzegovina and Guinea-Bissau.
The river network is extracted from the upstream drainage area dataset by
specifying a minimum threshold river size (in units of UDA). Identifying the
headwater of a river is no trivial task, with regional and climatic factors
playing a part (Montgomery and Dietrich,
1988; Tarboton et al., 1991). Previous work exploring optimal initiation
thresholds for geomorphological floodplain mapping found that DEMs with a
resolution of 1 arcsec (∼30 m) could use initiation
thresholds less than 10 km2 UDA. In the same study, a 3 arcsec
(∼90 m) resolution DEM was used with a 100 km2 UDA
threshold (Annis et al., 2019). The MERIT Hydro data we
use in this study have a resolution of 3 arcsec (∼90 m), but its incorporation of crowdsourced river data has optimized its
representation of small streams and rivers. As such, we use a globally
consistent river initiation threshold of 10 km2 UDA for the RFSM. This
is a large assumption as in some locations globally there will be no
visible channel at this location. However, we argue that removing areas of
potential exposure to avoid overprediction in some areas goes against the
premise of this study, which is to explore and identify “missed” areas of
exposure. The exposure calculations for small streams should therefore be
interpreted with these limitations in mind.
Once the river network has been extracted, the rivers in the network are
classified based on their Strahler stream orders (Strahler, 1957).
The Strahler stream order is a dimensionless indicator of the magnitude of
the river based on its hierarchy within the drainage basin.
The calibration basins shown on a map of the simplified Köppen–Geiger climate zones and the calibrated maximum relative elevation
difference to the nearest draining channel (Hn) for each Strahler
stream order in the four climate zones considered (polar regions are
excluded from the analysis).
Calibrating the river flood susceptibility map
The maximum relative elevation difference to the nearest draining channel,
Hn (see Fig. 1a), for each Strahler stream order (n) is the only RFSM
parameter requiring calibration. We use a variable H, which scales with
Strahler stream order, to account for changes in flood depth as a river's
size changes. In Samela et al. (2017), the best performing
geomorphic index also accounts for variations in river size by scaling
relative to the river's upstream contributing area.
To account for climatic variability in a river's flood susceptibility
(Smith et al., 2015), we split the globe into five simplified
Köppen–Geiger climate zones (Fig. 2): Tropical, Arid, Temperate, Continental,
and Polar. Polar regions are excluded from our analysis as these regions are
dominated by glacial but not fluvial processes (Chen et al., 2019). The
RFSM has uniquely calibrated Hn values in each of the four climate
zones. We calibrate the Hn values in 19 different basins (see
Fig. 2) spanning five different continents across all four climate zones considered. We use a combination of national, continental, and global flood hazard maps for calibration in each climate zone. This is to ensure that there is sufficient calibration data for each Strahler order river as only the national flood hazard data capture flooding for low-order rivers. To maintain consistency across the calibration data, we use 100-year return period flood hazard maps. Two different national
flood maps are used for calibration. The first is the National Flood Hazard
Layer (NFHL) produced by the Federal Emergency Management Agency (FEMA)
(https://www.fema.gov/flood-maps/tools-resources/flood-map-products/national-flood-hazard-layer, last access: 14 September 2021; FEMA, 2015).
NFHL data are used for calibration in North American basins including Puerto
Rico, Lower Gila, Upper Pecos, Lower Mississippi, Alabama, Muskingum, Rock,
and Susquehanna. The second national flood map is the Environment Agency's
100-year flood map for planning
(https://data.gov.uk/dataset/bed63fc1-dd26-4685-b143-2941088923b3/flood-map-for-planning-rivers-and-sea-flood-zone-3,
last access: 14 September 2021; Environment Agency, 2021), which is used
for calibrating the RFSM in the Thames basin in England. The continental
flood map for Europe (Dottori et al., 2016b), developed by the
Joint Research Centre (JRC), is used to calibrate the RFSM in the Jucar river
basin in Spain, the Loire river basin in France, the Po river basin in Italy
and Switzerland, and the Oder river basin in Poland, Germany, and Czech
Republic. A global flood hazard map (Dottori et al., 2016a),
also developed by the JRC, is used to calibrate the RFSM in the central
Amazon basin in Brazil; the lower Congo basin in the Democratic Republic of
Congo and the Republic of Congo; the Lower Mekong basin in Thailand,
Cambodia, Vietnam, and Laos; the Upper Nile basin in Egypt and Sudan; the
lower Lena basin in Russia and Kazakhstan; and the central Lena basin in
Russia. Maps of the reference flood maps used for calibration are shown in
Fig. S1 in the Supplement, and further details about each calibration basin can be found in Table S1 in the Supplement.
The values are calibrated in each climate zone by running thousands of
different combinations of Hn in each calibration basin. Optimal Hn
values are determined by using three commonly used measure-of-fit scores:
critical success index (CSI), hit rate (HR), and bias (Wilks, 2006). The
Hn values retained are the ones that result in the best fit scores with
respect to the reference flood maps within each climate zone. Final
calibrated Hn values for each climate zone are shown in Fig. 2. More detailed
information on the calibration of the RFSM can be found in Sect. S1 of the
Supplement.
Once Hn values for each order have been assigned, each stream order
is processed separately (Fig. 1b), and then merged together. In areas of overlap,
the highest order stream retains the values. Two datasets are produced as
output: a map of the flooded river's upstream drainage area and a map of
the flooded river's Strahler stream order. Illustrations of these two
outputs are shown in Fig. 1c.
Validating the river flood susceptibility map
The RFSM is validated against both existing GFMs and observed flood events.
Validation against GFMs is carried out for the whole African continent using
the 100-year return period aggregated output of six GFMs from a previous
model intercomparison study (Trigg et al.,
2016a). The six GFMs that make up the aggregated output include CIMA-UNEP
(Rudari et al., 2015), Fathom (Sampson et al., 2015), GLOFRIS
(Winsemius et al., 2013; Ward et al., 2013), JRC (Dottori et al.,
2016c), and U-Tokyo (Yamazaki et al., 2011). To assess the credibility of
the RFSM, it is also validated alongside an existing global geomorphological
floodplain map (Nardi et al., 2019). For validation
we split the African continent into eight major drainage basins (see Fig. S3 in the Supplement)
according to the HydroBASIN Level 2 classification (Lehner and Grill,
2013). The results of the GFM validation show that the RFSM produces
credible flood extents when compared with existing GFM outputs in Africa.
The RFSM correctly captures over 90 % of high-agreement flood zones (where
at least five out of six GFMs agree) in seven of the eight major drainage basins in
Africa. In the East African basin, the RFSM captures 87 % of this high-agreement flood zone. Comparing CSI, HR, and bias scores for the RFSM and the
existing global geomorphological floodplain map, the RFSM scores better in
all the major drainage basins in Africa except for North Africa (where both
maps score poorly due to the Sahara Desert). The RFSM is also validated
against observed flood events in Nigeria and Mozambique. The 2012 flooding
in Nigeria and the 2007 floods in Mozambique affected four million people
and over one hundred thousand people, respectively
(Bernhofen et al., 2018b). Validation data
for both these flood events used in a previous GFM validation comparison
study (Bernhofen et al., 2018a) are also used to
validate the RFSM. The RFSM is validated against observed data in three
validation regions: Lokoja, which is a narrow, confined floodplain at the
confluence of the Niger and Benue rivers in Nigeria; Idah, which is a flat and
extensive floodplain south of Lokoja; and Chemba, which is an anabranching
stretch of the Zambezi river just upstream of the delta in Mozambique.
Validation of the RFSM against observed data from these historical flood
events shows that it performs similarly to the best performing GFMs in each
of the three validation regions. Further detail about the validation of the
RFSM can be found in Sect. S2 of the Supplement.
It is important to note the limitations of our methodology and
geomorphological approaches in general. The RFSM does not account for flood
protection measures and cannot communicate the probability of flooding in
any location. It consistently represents a river's flood susceptibility
based on the surrounding terrain alone. In regions where the floodplain
boundaries are less distinguishable from the terrain, such as flat and
low-lying areas, geomorphological approaches are prone to overprediction as
they do not represent mass and momentum conservation. Our method's intended
use is as a model-independent global “first look” analysis to inform future
hydrodynamic model development and use.
Measuring exposure
We investigate the human exposure to river flood susceptibility. Human
exposure is herein defined as the intersection of our river flood
susceptibility map and a spatially distributed population layer. Three
population datasets are used to measure exposure: Facebook's High Resolution
Settlement Layer (HRSL) (https://data.humdata.org/organization/facebook?q=density, last access: 14 September 2021)
(Facebook and Ciesin, 2016), the European Commission Joint Research
Centre's Global Human Settlement Population (GHS-POP)
(10.2905/0C6B9751-A71F-4062-830B-43C9F432370F)
(Schiavina et al., 2019), and WorldPop
(10.5258/SOTON/WP00645) (Stevens et al., 2015). These
population datasets all use the same initial input census data, from GPWv4
(Center for International Earth Science Information Network –
Ciesin – Columbia University, 2016), but their methods for allocating the
population across gridded cells differ. Facebook's HRSL is the only dataset
of the three lacking full global coverage (at the time of writing 168
countries have been mapped). It is also the most recent, with work ongoing
to map the remaining countries. HRSL uses ultra-high-resolution commercial
satellite imagery (∼50 cm resolution) and convolutional
neural networks to detect individual buildings at the country level
(Tiecke, 2017). Subnational census data for the year 2018 is then
proportionally allocated to the identified buildings at 1 arcsec
resolution (∼30 m at the Equator).
Similarly to the HRSL in methodology, JRC's GHS-POP dataset identifies built-up areas from Landsat imagery and proportionally allocates census data to
the built-up areas (Freire et al., 2015). In regions where
no settlements can be identified but where census data indicate there is a
population, the population is evenly distributed across the census area
using areal weighting (Freire et al., 2016). This can occur in some
rural areas where small settlements are not captured by the Landsat
imagery. Despite being coarser in spatial resolution at 9 arcsec
(∼250 m at the Equator), GHS-POP provides consistent
multi-temporal population estimates (for the years 1975, 1990, 2000, and 2015) allowing for
accurate analyses over time (Freire et al.,
2020).
Unlike the other two population datasets, which evenly spread census data
over identified settlements, WorldPop uses a complex model to disaggregate
population over an area (Leyk et al.,
2019). It uses a random forest model and a number of ancillary datasets to
dynamically weight the distribution of census data over a 3 arcsec
(∼90 m at the Equator) gridded area (Stevens et al., 2015)
to produce annual population estimates from 2000–2020.
Exposure calculations necessitate uniformity between the intersecting
datasets in terms of spatial resolution. As such, the GHS-POP layer was
resampled from 9 arcsec resolution and the population was evenly distributed
to a 3 arcsec resolution grid to allow for analysis with a flood map of
the same resolution. Conversely, for the HRSL exposure calculations the RFSM
was resampled from 3 to 1 arcsec resolution. When comparing
the exposure results between population datasets, the epoch used for
comparison was 2015. National population totals for the HRSL and WorldPop
datasets for the years 2018 and 2015, respectively, were scaled relative to
GHS-POP 2015 national population totals.
Results and discussionGlobal exposure to different sized rivers from GHS-POP
Rivers were classified into six different sizes, expressed in upstream
drainage area (UDA) (km2), with the ranges increasing in powers of 10.
River classifications based on UDA, depicted in Fig. 3b for Nigeria, were as
follows: stream (10–100 km2), small river (100–1000 km2), medium
river (1000–10 000 km2), medium-large river (10 000–100 000 km2),
large river (100 000–1 000 000 km2), and huge river (>1 000 000 km2).
Flood exposure calculated with the Global Human Settlement
Population (GHS-POP) layer. (a) Top 50 most exposed countries in terms of
total flood exposure. (b) The river size classifications visualized in
Nigeria. (c) Top 50 most exposed countries in terms of normalized flood
exposure (normalized to country's total population).
Flood exposure is first calculated using the GHS-POP layer. Globally, we
find 1.94 billion people susceptible to flooding from rivers with a UDA
greater than 10 km2. Breaking this down by continent, Asia's flood
exposure is 1.49 billion, Africa's is 203 million, Europe's is 104 million,
North America's is 81 million, South America's is 59 million, and Oceania's
is 3.5 million. Splitting global flood exposure by river size, of the total
exposed, 18.2 % are from streams, 26.4 % from small rivers, 23.7 %
from medium rivers, 17.2 % from medium-large rivers, 8.4 % from large
rivers, and 6.1 % from huge rivers. Asia makes up over 75 % of the total
global flood exposure, the majority of this amount coming from India and
China, which are by far the two most exposed countries (see Fig. 3a). Roughly half
of India's flood exposure is from streams and small rivers. Comparably, in
China, this figure is closer to a third. This is likely due to the degree of
urbanization in both countries; the percentage of China's urban population
is double that of India's (Worldbank, 2018). Urban areas are
disproportionately located on large rivers due to the historical tendency
for settlements to form in areas fertile for farming and convenient for
transport (McCool et al., 2008). As such, a greater proportion of
flood exposure in China comes from larger rivers, whereas in India, a
greater proportion comes from rural exposure to smaller rivers. Rivers
classified as “huge” are only found in some countries, but often they make up a
large proportion of the national flood risk. For example, the Brahmaputra in
Bangladesh and the Nile in Egypt and Sudan are responsible for just under
half of the national flood exposure in their respective countries.
To identify countries with the most acute flood risk, exposure was
normalized against total national population (Fig. 3c). Suriname has the highest
normalized exposure, with 894 people exposed per 1000. The country's low-elevation relief, and its capital city situated on the banks of the Suriname
river near its outlet into the Atlantic Ocean, makes Suriname particularly
vulnerable to flooding (Worldbank, 2019). A total of 4 of the top 10 most
“normally” exposed countries are in south or southeast Asia. These include
Bangladesh, Cambodia, Thailand, and Vietnam. Flooding in these countries is
severe and annual, normally occurring each year during the monsoon season.
In Europe, the Netherlands has a high normalized exposure, 738 exposed per
1000. The Netherlands has a long history of flooding due to its low
elevation, flat terrain, and high population density. It also has the most
advanced flood defense systems in the world, designed to contain river water
levels with a probability of occurrence once every 1250 years
(Stokkom et al., 2005). Geomorphological approaches to flood
mapping, such as the RFSM, cannot model probabilities of occurrence and are
therefore unable to represent flood prevention measures
(Scussolini et al., 2016) and distinguish between defended and
undefended floodplain zones. Much of the exposed population in the
Netherlands, as well as other countries with flood protection, resides in the
defended area of a floodplain. This does not eliminate their risk of
flooding, but just reduces the probability of it. The severity of a flood event
when defenses fail can be catastrophic, resulting in high-velocity flows and
rapid inundation with little to no warning.
The top 50 exposed countries calculated using the WorldPop and HRSL datasets
are detailed in Figs. S13 and S14 in the Supplement, respectively. We also compare continental and global
flood exposure estimates from different sized rivers calculated using
GHS-POP and WorldPop in Table 1. It is not possible to compare these global results
with HRSL calculated exposure as it does not yet have global coverage.
Global exposure calculated using the WorldPop layer is 2.026 billion,
roughly 83 million larger than the global figure calculated using GHS-POP.
Differences in exposure between the two datasets are largest in Africa, Asia, and Oceania. We explore the implications of using different population
datasets for flood exposure calculations in greater detail in Sect. 3.3 of this
paper.
Comparison of continental and global flood exposure estimates from
different sized rivers calculated with Global Human Settlement Population
(GHS-POP) layer and WorldPop. Exposure is in millions of people.
Africa Americas Asia and Oceania Europe Global RiverGHS-WorldPopGHS-WorldPopGHS-WorldPopGHS-WorldPopGHS-WorldPopclassPOPPOPPOPPOPPOPStream33.242.138.8838.45260.53274.6920.6520.07353.26375.31Small41.0348.4336.7236.13409.21415.3126.6326.01513.59525.88Medium39.4143.4529.8430.28363.67384.7726.8426.72459.76485.22Medium-large34.2335.9120.9420.65260.44268.1318.6418.6334.25343.29Large25.3621.811.911.9114.14126.4611.411.5162.8171.66Huge30.4530.242.652.7486.4192.0900119.51125.07Total203.68221.93140.93140.151494.41561.45104.16102.91943.172026.43Exposure change from 1975–2015
An advantage of both the GHS-POP and WorldPop datasets is their population
estimates across different timescales, allowing for exposure analysis over
time. WorldPop has annual population maps from 2000–2020, and GHS-POP has
population estimates across four epochs: 1975, 1990, 2000, and 2015. Here, using
GHS-POP's multitemporal population layers, we calculate exposure change over
a period of 40 years. Normalized flood exposure estimates were calculated
for the years 1975, 1990, 2000, and 2015. These results are tabulated in
Table S10 in the Supplement. Population change is calculated by taking the difference between the
normalized exposure estimates for the years considered. Globally, total
flood exposure grew between 1975 and 2015 from 257 people per 1000 to 265
people per 1000. Interestingly, in both Tropical and Arid climates total
flood exposure over this 40-year period grew by 11 people per 1000, but in
Temperate and Continental climates total flood exposure decreased by 4 and
10 people per 1000, respectively. Developing countries are largely located
in tropical and arid climates, conversely, developed economies are prevalent
in temperate and continental climates. These findings correspond with
previous work done by Jongman et al. (2012), which found developing
countries had the largest increases in exposure relative to population
growth in the period 1970–2010. At the continental level, normalized flood
exposure saw the largest increase in Asia, growing by 15 people per 1000
from 1975–2015. It also grew in South America by 5 people per 1000. In
Europe, changes in normalized exposure over this period were negligible,
while in North America, Africa, and Oceania normalized exposure decreased by
3, 5, and 2 people per 1000, respectively. Comparing these results with a
related study by Ceola et al. (2014), which used satellite
night-time light intensity to explore changes in river flood exposure from
1992–2012, we find similar trends in North America, South America, Europe,
and Asia. Exposure over the period 1975–2015 increased for streams,
medium-large, large and huge rivers. There were slight reductions in
exposure for small and medium sized rivers.
Exposure changes at the national level are depicted in Fig. 4. The highest
increase in overall flood exposure was seen in Nepal and French Guinea. In
both countries, the proportion of exposed population grew by 200 people per
1000 in the period 1975–2015. In French Guinea, this sudden increase is
largely due to the population growth of Saint-Laurent-du-Maroni, a town
situated on the banks of the Maroni river. From 1975–2015 the town's
population grew 1800 % compared with the national population growth of
360 %. In Nepal, one of the top 10 fastest urbanizing countries in the
world (Bakrania, 2015), the flood exposure growth is a result of this
fast urbanization in cities such as Kathmandu, which is intersected by eight
different rivers. An exposure decrease of 172 people per 1000 was seen in
South Sudan. This is due to the growth of urban areas outside the Sudd swamp
in cities such as Juba, Yei, Yambio, Nzara, and Wao. South Sudan has been
hit by devastating floods in the past year, which displaced over 800 000
people (OCHA, 2020). Had relative population exposure in South Sudan
grown, rather than shrunk, the recent flooding could have been even worse.
Country-level river flood exposure (population normalized) change
from 1975–2015 calculated using the Global Human Settlement Population
(GHS-POP) layer. River size expressed in upstream drainage area (UDA).
Exposure estimates from different population datasets
Exposure differences arising from the use of different population layers
were calculated for the 168 countries where all three population datasets
are available (Fig. 5) (see Table S11 in the Supplement for a list of the missing countries). In the countries
examined, normalized exposure (with respect to the country's total
population) calculated with WorldPop data was the highest (270 exposed per
1000), followed by GHS-POP (256 exposed per 1000), and HRSL exposure was the
lowest (235 exposed per 1000). These findings correlate with a previous
study by Smith et al. (2019) which found WorldPop data overestimated
flood exposure compared to HRSL data in each of the 18 developing countries
examined.
Flood exposure comparison in 168 countries using the High
Resolution Settlement Layer (HRSL), WorldPop layer, and Global Human Settlement
Population (GHS-POP) layer. (a) Comparison of the total normalized flood
exposure between the three population datasets in all available countries.
(b) How the calculated exposure figures differ per river size
classifications. (c) Country-level statistics for average normalized
exposure (calculated as the mean of the three national exposure estimates)
and the sensitivity of the exposure calculation to the choice of population
dataset (measured as the absolute range of the three national exposure
estimates). The higher up the y axis and x axis are, the greater the average
exposure and sensitivity will be to the choice of population dataset, respectively.
Differences in calculated exposure across the river sizes are shown in
Fig. 5b. Exposure differences were most pronounced for smaller rivers (streams,
small, and medium rivers), while there was almost no exposure difference for
the largest river class (huge). The overall trend across all river sizes
consistently shows that WorldPop estimated the highest exposure, followed by
GHS-POP, and HRSL estimated the lowest exposure.
The population mapping approaches of the three population layers can go some
way towards explaining the differences in calculated exposure; these
corresponding outputs are visualized in Fig. 6, in which we qualitatively compare the
population distribution of the three outputs with respect to the settlement
distribution, manually identified from high-resolution satellite imagery,
along the Likouala aux Herbes river in the Republic of Congo. WorldPop's
population distribution algorithm dasymetrically redistributes the whole
population across the grid, also in areas where no settlements have been
identified. This is done under the assumption that not all “built-up” areas
will be picked up in the satellite imagery (TReNDS, 2020). When
intersected with a flood extent, such a modeling approach can lead to
misestimation of flood exposure in rural areas with respect to the other
two population datasets. In the area examined in Fig. 6, WorldPop estimates 1167
people exposed, compared with 17 581 and 13 789 people exposed estimated by
HRSL and GHS-POP, respectively. This is despite WorldPop exposure covering
over 93 % of the area examined, which far exceeds GHS-POP's 5 % exposed
area and HRSL's 1 % exposed area. WorldPop's approach to rural population
distribution can lead to underestimation of exposure in small rural
settlements (such as in Fig. 6) or overestimation of exposure across large
expansive areas of flooding, as will be explored later in this section.
Conversely, the approach implemented by both GHS-POP and HRSL (which spread
census data only over identified “built-up” areas) is more sensitive to
omission and commission errors arising from the classification of
settlements (Palacios-Lopez et al., 2019). For example, undetected
settlements outside the flood extent would result in artificially higher
flood exposure estimates as the underlying census data are only spread across
the identified settlements (a greater proportion of which are
now identified as being within the flood extent). Similarly, commission errors
(false positives) are common in sandy or rocky landscapes and often occur in
coastal areas or along riverbanks. Commission and omission errors can lead
to either artificial increases or decreases in flood exposure estimates,
depending on the location of these errors with respect to the flood extent.
The resolution of the population layers should also be considered. GHS-POP's
fairly coarse (9 arcsec) resolution means that in some areas where the
potential for flooding (or not) falls within the resolution of a 9 arcsec grid cell, the settlement's avoidance (or not) of the flood risk
cannot be accurately represented. This effect can be reduced by upsampling
and proportionally reallocating the population to a grid that matches the
resolution of the flooded data, as we have done in this study. Similarly,
the spatial resolution of the underlying satellite imagery should be
considered. Both GHS-POP and WorldPop identify settlements using Landsat
imagery at 30 m resolution, while HRSL identifies settlements using
DigitalGlobe imagery at 0.5 m resolution. Previous work by Tiecke
(2017) showed that HRSL was able to identify buildings missed by GHS-POP,
highlighting the importance of high-resolution imagery for comprehensive
building classification.
The use of different population datasets had a negligible effect on exposure
estimates for the huge river class. Large settlements tend to form around
rivers of this size and on coastlines where rivers of this size drain.
Large urban areas are easily identifiable from remote sensing data, which
means the population distribution (and resulting exposure estimates) for
these urban centers shows less variation between the datasets. Conversely,
non-urban flood exposure estimates to smaller rivers show greater sensitivity
to the choice of population layer. This is because the approach to non-urban
population mapping between the three datasets differs. WorldPop, as mentioned
previously, distributes administrative-level census data across all 3 arcsec pixels in order to mitigate the impacts of potential omission and
commission errors in the settlement data. This approach leads to some
overestimation in rural populations (Smith et al., 2019; Wardrop et al.,
2018). GHS-POP, which distributes census data over Landsat-identified
settlements (and in non-built-up areas distributes population at the census
unit by areal weighting), tends to underestimate rural populations. (Liu
et al., 2020; Leyk et al., 2019). HRSL's use of ultra-high-resolution
satellite imagery has been shown in previous studies to accurately identify
rural settlements (Tiecke, 2017; Smith et al., 2019).
However, the method of proportional allocation used to distribute the census
data is relatively crude. Uncertainties in the underlying census data should
also be considered as the quality and detail of the data, as well as the
frequency at which it is collected, vary significantly at the national
level (Leyk et al., 2019). The three
population datasets compared in this study share the same input census data
(GPWv4) and therefore any associated census uncertainties are a common
feature shared across the three datasets.
Calculating the general trends of exposure between the population layers is
useful for making broad conclusions about the suitability of a population
layer. Understanding the variations in the data at the country level leads
to more actionable information about the appropriate use of different
population layers. We calculate both the severity of flooding in each
country (as the mean of the normalized national flood exposure estimates
calculated with the three population datasets) and the disagreement between
the population exposure estimates in each country (as the absolute range of
the three normalized national flood exposure estimates). The disagreement
between the population-layer exposure estimates for each country varies
significantly (Fig. 5c). In the three countries with the highest exposure
disagreement (Belize, the Republic of Congo, and Guinea-Bissau) WorldPop
estimates of exposure are far greater than either HRSL or GHS-POP estimates.
In Belize, a country with large areas of inundated wetlands, WorldPop
estimates 135 000 people exposed, while GHS-POP and HRSL estimate 70 000 and
80 000 exposed, respectively. In the Republic of Congo, a country with large
areas of floodplain, WorldPop estimates 1.3 million people exposed, and
GHS-POP and HRSL estimate 810 000 and 780 000 exposed, respectively.
WorldPop's method of distributing the population over a large area results
in significant overestimation compared with HRSL or GHS-POP in these rural
inundated areas. This can be seen in greater detail in Fig. 7 for Guinea-Bissau. In
Guinea-Bissau, GHS-POP and HRSL (which estimate exposures of 180 000 and
160 000, respectively) identify settlements largely situated outside the
floodplains (“dry” cells in blue). Comparatively, WorldPop's modeling
approach and assumptions lead to far more “wet” population cells and an
estimate of exposure (480 000) more than double that of the other two
population layers. The exposure disagreement in these three countries is
compounded by the relatively large areas of inundation in each country. The
percentage of inundated area is 25 %, 30 %, and 26 % for Guinea-Bissau,
Belize, and the Republic of Congo, respectively. In comparison, the
percentage of populated area defined by the population layers is less than
5 % for GHS-POP and HRSL but more than 95 % for WorldPop in each of the
three countries. As exposure in this study is defined as the intersection of
the flooded area and the populated area, it is understandable that
WorldPop's exposure estimates are more sensitive to the area of inundation.
This is evident when examining a country with high exposure disagreement but
with a comparatively smaller area of inundation. In Bosnia and Herzegovina
(Fig. 7), the percentage of flooded area is just 9 %, and the GHS-POP layer
estimates far greater exposure (1 million) than either WorldPop (680 000) or
HRSL (610 000). Here, where much of the exposure occurs near the banks of
the rivers, the coarse spatial resolution of GHS-POP is less able to
precisely locate settlements situated just outside the floodplain. As a
result, more populated cells are flagged “at risk” compared to the higher-resolution HRSL layer.
Comparison of population datasets and their intersection with the
flood extent in Bosnia and Herzegovina and Guinea-Bissau. The top two
insets show the river flood susceptibility map (RFSM) split into the
different river size categories for the whole country (top panel) and for a
smaller, more detailed area of both countries (second panel from top). The
remaining insets show the three different population maps and their
intersection with the flood map in the detailed areas of both countries.
Blue cells indicate the population cells are dry (not exposed to flooding),
and red cells indicate the population cells are wet (exposed to flooding).
These results have shown that the use of different population layers can
lead to vastly different flood exposure estimates because of inherent
differences in their spatial resolutions, methods used, and assumptions made
to produce them. Our comparative analysis has identified in which countries
exposure calculations are sensitive to the choice of population layer and
shed light on some of the reasons for exposure disagreement. However, there
is a limit to the conclusions that can be drawn from comparative analyses
alone, and there is an urgent gap for more studies which validate the
accuracy of these population layers using ground-truthed data.
It would be imprudent to definitively recommend one population dataset for
use in flood exposure studies without extensive comparative global
validation. However, previous studies have shown that HRSL performs better
than existing population datasets at mapping reference building footprints,
especially in rural areas (Tiecke, 2017; Smith et al., 2019).
Our results also point to some of the benefits of using HRSL. Its settlement
identification method for population distribution avoids exposure
overprediction common in other population data, and its high resolution can
better capture the accurate location of settlements. Despite this, HRSL
should not be considered a catchall dataset for flood exposure. Its high
resolution may limit its use in certain situations due to computational
restraints. Similarly, in studies of flood risk over time population data
with multiple temporal epochs, such as GHS-POP or WorldPop, are better
suited. The results we present in this section and Fig. 5 are intended to inform
users of these population datasets about their appropriate use. In countries
with high exposure disagreement, the choice of population dataset for flood
exposure should be carefully considered, and further accuracy assessments of
the population layers are recommended.
Relevance to global flood models
The minimum size of river represented in global flood risk models (GFMs)
varies (see Table 2), with minimum river size thresholds ranging between 50–5000
km2 UDA, which is 3 orders of magnitude. River network size can be limited
by the granularity of input data such as rainfall (Dottori et al.,
2016c), or by the computational demand of modeling floods at the global
scale.
Global flood model river representation.
Minimum river size (upstream drainage area)Global flood risk modelRiver sizes modeled (P = partial)50 km2Fathom (Sampson et al., 2015)Stream (P), small, medium, medium-large, large, huge500 km2ECMWF (Pappenberger et al., 2012) and U-Tokyo (Yamazaki et al., 2011)Small (P), medium, medium-large, large, huge1000 km2CIMA-UNEP (Rudari et al., 2015)Medium, medium-large, large, huge5000 km2JRC (Dottori et al., 2016c)Medium (P), medium-large, large, huge
Differences in river network size between GFMs undoubtedly lead to
differences in global flood exposure estimates. These differences can be
even more pronounced at the national level where GFMs have been used to
inform disaster risk management (Ward et al., 2015). Flood exposure was
calculated for the different GFM river thresholds using the GHS-POP layer.
Globally, we found that exposure estimates between the river threshold which
results in the largest river network (>50km2 UDA) and
the river threshold which results in the smallest river network
(>5000km2 UDA) differ by over a factor of 2. If the size
of the river network was further increased by reducing the river threshold
to 10 km2 UDA (below current GFM representation), the exposed
population captured increases by 13 %.
At the national level, in countries such as Suriname, the Republic of Congo,
and Egypt, the greatest proportion of flood risk is posed by rivers with a
UDA of 5000 km2 or greater. In these countries, GFMs could be used
interchangeably. Understanding which rivers pose a significant flood risk is key to accurately representing national flood risk. In Benin, for
example, the estimated flood exposure when a 5000 km2 UDA threshold is
applied is 0.49 million people. When the threshold is reduced to 1000
km2 UDA, the estimated exposure increases to 1.8 million people. Some
countries do not have large rivers flowing through them, and the flood risk
will result entirely from smaller rivers. Often these are island nations,
such as in Jamaica or Trinidad and Tobago, where all flood risk is from
rivers smaller than UDA 1000 km2. However, in Andorra for example, a
landlocked country, to capture any flood exposure, a 50 km2 UDA
threshold is needed.
To aid national-level flood risk practitioners in their choice of GFM, we
calculated the minimum river threshold required to capture a given
percentage of the largest river network's (>50km2 UDA)
national exposure. Exposure percentages ranging from 10 %–90 % were
calculated for each of the three population datasets used in this study and
mapped for each nation, globally. All 27 maps are included in
Figs. S15–S17 in the Supplement. Figure 8, which
shows the minimum river threshold required to capture at least 50 % of
possible GHS-POP exposure, illustrates these results. The map shows that
while in some countries GFMs could be used interchangeably, in others, the
size of the river network could significantly impact national flood exposure
estimates.
In which countries is the choice of river threshold important? The
map shows the global flood model (GFM) river upstream drainage area (UDA)
threshold required to capture over half a country's total flood exposure. In
dark green countries the choice of threshold is less important than in
orange countries. Grey areas are no-data regions. The map was calculated
using the Global Human Settlement Population (GHS-POP) layer. See Figs. S15–S17 for maps calculated with the other two global population layers and
for different percentages of total national exposed population.
It is difficult to exhaustively compare global flood exposure estimates from
previous GFM studies as often exposure is expressed differently (e.g.
expected annual exposure (EAE) vs. exposure to a return period flood) and
sometimes global exposure is not reported at all. In the comparable studies,
there is significant variation in global flood exposure estimates. In
Ward et al. (2013) global EAE was calculated at 169 million. This figure
is almost triple the 58 million calculated by Dottori et al. (2018) and
the 54 million calculated by Alfieri et al. (2017). In studies reporting
exposure to a 100-year flood, Hirabayashi et al. (2013) estimate 847
million people exposed, and Jongman et al. (2012) estimate 805 million
exposed.
The need for independent model comparison studies was met by Trigg et al.
(2016b) and Aerts et al. (2020) who compared GFM
output in Africa and China, respectively. These studies compared the output
of multiple GFMs, finding large disagreement between the modeled flood
extents. Both studies also found large variations in calculated exposure.
However, differences in exposure calculated by the GFMs were found to be
influenced just as much by different model forcings and resolutions as by
differences in river network size. Uncertainty in GFMs needs to be explored
across the model cascade to identify where the models need to improve.
Studies such as that of Zhou et al. (2021), which explores
uncertainty in model forcing, and this study, which explores uncertainties
in river network size, are important steps in directing future model
development.
Granularity of input data is the main obstacle to increasing river network
size in GFMs. The terrain data in all these models, which strongly
influence their performance, are derived from the Shuttle Radar and
Topography Mission (SRTM), a mission over two decades old (Farr et al.,
2007). New, 1 arcsec resolution (∼30 m at the Equator)
global DEMs have recently been released by both the National Aeronautics
and Space Administration (NASA) and the European Space Agency (ESA). The ESA
DEM is particularly important as its elevation is based on newer satellite
data from TanDEM-X. A new method for deriving an elevation map from
satellite images has also been developed by Google, capable of generating
DEMs at 1 m resolution (Nevo, 2019). Whether it is terrain or
climatology data, new and improved methods are constantly being developed
and better datasets are being released. There is scope in the near future
for increasing river network size in GFMs. This comes at a computational
cost, however, whether it is the use of a higher-resolution DEM or the
exponential increase in the number of rivers to model when the threshold river
size is reduced. Understanding where the representation of smaller rivers is
needed most, namely in areas of high exposure, would streamline the future
development of GFMs, targeting improvements in areas where flood risk is
highest.
Conclusions
This study has presented the first global picture of flood exposure
categorized by different sized rivers. We introduced a simple
geomorphological approach to delineating a river's flood susceptibility,
which is suitable for global-scale “first look” studies such as this and,
importantly, allows for an assessment of river network size independent of
global flood model structural and computational limitations. We find that
over 75 % of the global flood exposure is in Asia, with China and India
making up a significant proportion of this total. Streams (10–100
km2 UDA) and small rivers (100–1000 km2 UDA) are responsible for over
half of India's flood risk. At the global scale, these rivers contribute to
45 % of total flood exposure, emphasizing the importance of the
incorporation of these smaller rivers into global flood risk studies. We
find that large increases and decreases in flood exposure over the last
40 years are a result of urbanization, either inside the flood risk zone
or outside of it. The effect that the choice of population dataset had on
exposure calculations differed between countries. Globally, this effect was
most pronounced on smaller rivers, suggesting future studies that
incorporate these smaller rivers should be careful in their choice of
population data. Global flood models, the current tools for examining global
flood risk, differ significantly in the size of their river networks. We
found that the global flood exposure estimates differed by more than a
factor of 2 when calculated using the GFM river threshold that results in
the largest river network (UDA>50km2) compared to the
river threshold that results in the smallest river network (UDA>5000km2). These differences were often more pronounced at
the national level.
The results of this study are intended to inform both the developers and
users of global river flood models. Consideration of river network size and
how this relates to exposure is imperative to have a comprehensive
picture of flood risk. Increasing the size of the river network comes with
both data and computational restraints. Increasing the resolution of the
models (from 1 km to 90 m to 30 m) requires an order of magnitude increase in
computing power. Finer-resolution grids are imperative for representing
small streams accurately. This has big implications for models currently
operating at coarse resolution. Modeling smaller rivers requires not only
detailed high-resolution data but also efficient modeling structures
capable of running at higher resolutions. Understanding where the
representation of small rivers is needed most (areas of high exposure) can
focus future model development. Similarly, accurate flood exposure estimates
necessitate accurate population data. We have shown that the choice of
population data used in exposure calculations can have an enormous impact on
flood exposure estimates, and we have identified in which countries this
disagreement is most extreme and some of the reasons for
this. Flood risk practitioners should use these results as guidance about
which population layer is best suited for their locality and use. There is
need for further research in this area incorporating more population data
as these layers play such an integral role in flood exposure calculations.
In addition to more comparative analyses, there is also an urgent need for
these population data to be validated at the global scale with actual data
collected on the ground. Only then can definitive conclusions be drawn about
the appropriate use of different population datasets. The selection of GFMs
available to the end user is large and increasing. However, differences in
the size of river networks between the models can have a significant impact
on flood exposure estimates. While available GFMs could be used
interchangeably in some countries, in others, discrepancies in river network
size would lead to vastly different national flood exposure estimates. The
results of this study should help to inform GFM users about the appropriate
choice of GFM for their country of interest.
Data availability
All the data used in this study are freely available to download. The river flood susceptibility maps are available from the University of Leeds at
10.5518/947 (Bernhofen et al., 2021). Facebook's High Resolution
Settlement Layer can be downloaded by following the instructions in this
link https://data.humdata.org/organization/facebook?q=density (Humanitarian Data Exchange, 2021; Facebook and CIESIN, 2016).
The GHS-POP data can be downloaded here
10.2905/0C6B9751-A71F-4062-830B-43C9F432370F (Schiavina et al., 2019). The WorldPop
data can be downloaded here
10.5258/SOTON/WP00645 (Worldpop, 2018).
The supplement related to this article is available online at: https://doi.org/10.5194/nhess-21-2829-2021-supplement.
Author contributions
MVB and MAT conceived of the study. MVB designed and carried out the
analysis. MAT, PAS, CCS, and AMS supervised the project. MVB drafted the
manuscript. All authors contributed towards the discussion and editing of
the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work was undertaken on ARC3, part of the
high-performance computing facilities at the University of Leeds, UK. The
authors would like to thank the members of the Global Flood Partnership, who
have helped to shape this research through discussions and feedback at
numerous GFP workshops.
Financial support
This research has been supported by the UK Research and Innovation and iCASE funding from Fathom Global (grant no. NE/R008949/1) and by the UK Research and Innovation's Global Challenges Research Fund (grant no. ES/S008179/1).
Review statement
This paper was edited by Kai Schröter and reviewed by Serena Ceola and one anonymous referee.
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