New estimates of flood exposure in developing countries ... · New estimates of flood exposure in...
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New estimates of flood exposure in developing
countries using high-resolution population data
Jeff Neal1,2,
Andrew Smith2, Chris Sampson2, Niall Quinn2, Paul Bates1,2
1 School of Geographical Sciences, University of Bristol, UK
2 Fathom Global, Engine Shed, Bristol, UK
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Overview of findings
• Current estimates of global flood
exposure are made using data sets that
distribute population counts across large
areas of lowland floodplain.
• When intersected with simulated water
depths, this results in a significant mis-
estimation of flood risk.
• Here, we use new highly resolved
population information from the
Facebook/Columbia University High
Resolution Settlement Layer (HRSL)
and hazard data from Fathom.
• We find that humans make more
rational decisions about flood risk than
current demographic data suggest.
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Overview of presentation
• Flood hazard data
• Fathom global flood model
• 1 in 100 year return period
• Intersect with population
• Worldpop
• LandScan
• HRSL
• Breakdown results into
urban, semi-urban or rural
• Global Human Settlement
Layer
Sampson, C. C., A. M. Smith, P. D. Bates, J. C. Neal, L. Alfieri, and J. E.
Freer (2015), A high-resolution global flood hazard model, Water Resour.
Res., 51, 7358–7381, doi:10.1002/2015WR016954.
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How the global flood model works
Smith, A., C. Sampson, and P. Bates (2015), Regional flood
frequency analysis at the global scale, Water Resour. Res., 51,
539–553, doi:10.1002/2014WR015814.
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How the global flood model works
Neal, J., G. Schumann, and P. Bates (2012), A subgrid
channel model for simulating river hydraulics and floodplain
inundation over large and data sparse areas, Water
Resour. Res., 48, W11506, doi:10.1029/2012WR012514.
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St Louis Stage Validation
Illinois River at Hardin(1 in 1000) 43.3 ft(1) 42.4 ft on 08/03/1993(1 in 100) 39.3 ft(2) 38.2 ft on 04/29/1973(3) 36.7 ft on 05/29/1995(4) 36.5 ft on 04/14/1979(5) 36.3 ft on 04/26/2013(6) 35.8 ft on 06/28/2008(7) 34.8 ft on 05/16/2002(8) 34.6 ft on 04/27/1993(9) 34.5 ft on 10/09/1986(10) 34.0 ft on 03/09/1985(1 in 10) 33.8 ft
Mississippi River at St Louis(1 in 1000) 51.6 ft (1) 49.6 ft on 08/01/1993(1 in 100) 48.1 ft(2) 43.2 ft on 04/28/1973(1 in 10) 42.8 ft (3) 42.0 ft on 04/01/1785(4) 41.9 ft on 05/22/1995(5) 41.3 ft on 06/27/1844(6) 40.5 ft on 06/04/2013(7) 40.3 ft on 07/02/1947(8) 40.2 ft on 07/22/1951(9) 39.3 ft on 12/07/1982(10) 39.1 ft on 10/09/1986
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Recent large-scale flood exposure analysesModel/ study name Hydrology component Flow routing
component
Inundation data resolution
after downscalingPopulation data set Population data resolution
GLOFRIS
Ward et al.3
PCR-GLOBWB (0.5 degree) driven by
EU-WATCH reanalysis 1960–1999
Kinematic wave
0.5 deg
30 arc sec
~900 m
LandScan™
Bhaduri et al.14
30 arc sec
~900 m
CaMa-UT
Hirabayashi et al. 37
MATSIRO-GW (1 degree)-driven by
JRA-25 Reanalysis 1979-2010
+GPCP rain gauge correction
Inertial wave
0.25 deg
18 arc sec
~540 m
Gridded Population of the
World (GPW) version 3
CIESIN and CIAT38
2.5 arc minutes
~4500 m
CIMA-UNEP
(GAR, 39)
Regional FFA from global gauge data
+ ECEarth bias corrected
Manning’s equation at
multiple points
3 arc sec
~90 m
LandScan™
Bhaduri et al. 14
Data aggregated to 1x1km
within 10km of a coastline,
5x5km elsewhere
GLOFRIS, CaMA-UT,
CIMA-UNEP, Fathom-
Global90 (formerly known
as SSBN), JRC, ECMWF
Trigg et al. 10
Various Various 3-30 arc sec
~90-900 m
WorldPop
Stevens et al. 15
Data aggregated to 30 arc
sec resolution to match
coarsest model output
~900 m
CaMa-UT
Kinoshita et al. 40
MATSIRO-GW (1 degree)-driven by
JRA-25 Reanalysis 1979-2010
+GPCP rain gauge correction
Inertial wave
0.25 deg
18 arc sec
~540 m
History Database of the
Global Environment (HYDE)
version 3.1 for the year 2005
Goldewijk et al. (2010)18
5 arc minutes
~10 km
Fathom-US
Wing et al 21
Regional FFA from global gauge data Inertial wave
1 arc second ~30 m
No downscaling
1 arc sec
~30 m
US Environme-ntal
Protection Agency (EPA)
EnviroAtlas
Pickard et al. 41
1 arc sec
~30m
A non-exhaustive summary of recent large-scale flood risk analyses and the
population data sets used (after Trigg et al.).
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Population exposure datasets
• Landscan
• Dasymetric downscaling approach using spatial data and imagery to
disaggregate census data to a regular grid, 30 arc sec, ~900 m
• Worldpop
• Dasymetric downscaling based on land use and other data to
disaggregate census data to a regular grid, 100 m
• HRSL
• High resolution (1 arc second, ~30 m) population density data derived
using Convolutional Neural Network techniques and 0.5 m resolution
satellite imagery capable of resolving individual buildings.
• Census data redistributed to buildings.
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Population exposed to the 1 in 100 year
flood (millions).
Country WorldPop HRSL Change % LandScan™ HRSL Change %
Burkina Faso 2.31 1.74 -25 2.95 1.74 -41
Cambodia 6.00 4.69 -22 6.76 4.69 -31
Ghana 3.16 2.64 -16 3.65 2.64 -28
Haiti 3.15 3.11 -1 2.82 3.11 10
Madagascar 4.29 3.50 -18 4.45 3.50 -21
Malawi 2.57 1.61 -38 2.68 1.61 -40
Mexico 27.03 24.37 -10 28.69 24.37 -15
Mozambique 5.10 3.76 -26 5.80 3.76 -35
Philippines 44.00 42.65 -3 47.61 42.65 -10
Puerto Rico 0.79 0.68 -15 0.82 0.68 -18
Rwanda 0.79 0.59 -25 1.14 0.59 -48
South Africa 2.88 2.02 -30 4.66 2.02 -57
Sri Lanka 3.78 2.84 -25 4.67 2.84 -39
Tanzania 7.24 5.29 -27 7.59 5.29 -30
Uganda 4.17 1.66 -60 3.99 1.66 -58
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Population exposure examples
Analysis snapshot in Cambodia: A) WorldPop data B) Facebook HRSL C)
1in100 year flood hazard intersecting with WorldPop, D) 1in100 year flood
hazard intersecting with Facebook HRSL
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Population exposure examples
Example of population data and generated exposure maps for Lilongwe city, in
Malawi. A, B & C displays the HRSL, WorldPop and LandScan™ population
datasets respectively. D, E &F shows the exposure to the 100 year flood hazard
footprint for each demographic dataset respectively.
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Cumulative distribution of exposure
Cumulative distribution of exposed population across all of the modelled ‘wet’ cells. Red indicates WorldPop exposure, Green the LandScan™ exposure and Blue the exposure calculated using the HRSL data.
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Exposure to flooding across land use type
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Proportion of area returning an exposure
value, across each land use type
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Population exposed to the 1 in 100 year
flood (millions).
Country WorldPop HRSL Change % LandScan™ HRSL Change %
Burkina Faso 2.31 1.74 -25 2.95 1.74 -41
Cambodia 6.00 4.69 -22 6.76 4.69 -31
Ghana 3.16 2.64 -16 3.65 2.64 -28
Haiti 3.15 3.11 -1 2.82 3.11 10
Madagascar 4.29 3.50 -18 4.45 3.50 -21
Malawi 2.57 1.61 -38 2.68 1.61 -40
Mexico 27.03 24.37 -10 28.69 24.37 -15
Mozambique 5.10 3.76 -26 5.80 3.76 -35
Philippines 44.00 42.65 -3 47.61 42.65 -10
Puerto Rico 0.79 0.68 -15 0.82 0.68 -18
Rwanda 0.79 0.59 -25 1.14 0.59 -48
South Africa 2.88 2.02 -30 4.66 2.02 -57
Sri Lanka 3.78 2.84 -25 4.67 2.84 -39
Tanzania 7.24 5.29 -27 7.59 5.29 -30
Uganda 4.17 1.66 -60 3.99 1.66 -58
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Population exposed to the 1 in 100 year
flood (millions).
Country WorldPop HRSL Change % LandScan™ HRSL Change %
Burkina Faso 2.31 1.74 -25 2.95 1.74 -41
Cambodia 6.00 4.69 -22 6.76 4.69 -31
Ghana 3.16 2.64 -16 3.65 2.64 -28
Haiti 3.15 3.11 -1 2.82 3.11 10
Madagascar 4.29 3.50 -18 4.45 3.50 -21
Malawi 2.57 1.61 -38 2.68 1.61 -40
Mexico 27.03 24.37 -10 28.69 24.37 -15
Mozambique 5.10 3.76 -26 5.80 3.76 -35
Philippines 44.00 42.65 -3 47.61 42.65 -10
Puerto Rico 0.79 0.68 -15 0.82 0.68 -18
Rwanda 0.79 0.59 -25 1.14 0.59 -48
South Africa 2.88 2.02 -30 4.66 2.02 -57
Sri Lanka 3.78 2.84 -25 4.67 2.84 -39
Tanzania 7.24 5.29 -27 7.59 5.29 -30
Uganda 4.17 1.66 -60 3.99 1.66 -58
Total exposed population range between 3.12M and
3.01M for the WorldPop and HRSL
A small change of -4%.
In WorldPop data this exposure is spread over an area of
around 40,000 km2, compared with an area of around
3700 km2 when using HRSL data.
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Population exposed to the 1 in 100 year
flood (millions).
Country WorldPop HRSL Change % LandScan™ HRSL Change %
Burkina Faso 2.31 1.74 -25 2.95 1.74 -41
Cambodia 6.00 4.69 -22 6.76 4.69 -31
Ghana 3.16 2.64 -16 3.65 2.64 -28
Haiti 3.15 3.11 -1 2.82 3.11 10
Madagascar 4.29 3.50 -18 4.45 3.50 -21
Malawi 2.57 1.61 -38 2.68 1.61 -40
Mexico 27.03 24.37 -10 28.69 24.37 -15
Mozambique 5.10 3.76 -26 5.80 3.76 -35
Philippines 44.00 42.65 -3 47.61 42.65 -10
Puerto Rico 0.79 0.68 -15 0.82 0.68 -18
Rwanda 0.79 0.59 -25 1.14 0.59 -48
South Africa 2.88 2.02 -30 4.66 2.02 -57
Sri Lanka 3.78 2.84 -25 4.67 2.84 -39
Tanzania 7.24 5.29 -27 7.59 5.29 -30
Uganda 4.17 1.66 -60 3.99 1.66 -58
In Malawi, around 80% of modelled wet cells
overlay inhabited areas according to the WorldPop
and LandScan™ population data, compared with
only around 2% when the HRSL population data
are used.
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Conclusions
• In this analysis we use flood hazard data from a ~90m resolution
hydrodynamic inundation model to demonstrate the impact of different
population distributions on flood exposure calculations for 18
developing countries spread across Africa, Asia and Latin America.
• In the new data, populations are represented as more risk-averse and
largely avoiding obvious flood zones.
• The results also show that existing demographic datasets struggle to
represent concentrations of exposure, with the total exposed
population being spread over larger areas.
• A substantial shift in exposure from rural to urban communities is
observed with the HRSL data
• The results suggest that many large-scale flood risk estimates may
require significant revision.