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

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.

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.

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.

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.

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

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.).

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.

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

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

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.

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.

Exposure to flooding across land use type

Proportion of area returning an exposure

value, across each land use type

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

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.

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.

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.