Global riverine flood risk – how do hydrogeomorphic  floodplain maps compare to flood hazard maps?

Lindersson, Sara; Brandimarte, Luigia; Mård, Johanna; Di Baldassarre, Giuliano

doi:https://doi.org/10.5194/nhess-21-2921-2021

Articles | Volume 21, issue 10

https://doi.org/10.5194/nhess-21-2921-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-21-2921-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 21, issue 10

Research article

|

30 Sep 2021

Research article |

| 30 Sep 2021

Global riverine flood risk – how do hydrogeomorphic floodplain maps compare to flood hazard maps?

Sara Lindersson, Luigia Brandimarte, Johanna Mård, and Giuliano Di Baldassarre

Data sets

GFPLAIN250m F. Nardi and A. Annis https://doi.org/10.6084/m9.figshare.6665165.v1

Flood hazard map of the World - 500-year return period F. Dottori, L. Alfieri, P. Salamon, A. Bianchi, L. Feyen, and F. Hirpa http://data.europa.eu/89h/jrc-floods-floodmapgl_rp500y-tif

Flood hazard map of the World - 100-year return period F. Dottori, L. Alfieri, P. Salamon, A. Bianchi, L. Feyen, and F. Hirpa http://data.europa.eu/89h/jrc-floods-floodmapgl_rp100y-tif

Flood Hazard Model for the Global Assessment Report 2015 CIMA Research Foundation and UNEP https://preview.grid.unep.ch/

HydroATLAS version 1.0 B. Lehner, S. Linke, and M. Thieme https://doi.org/10.6084/m9.figshare.9890531.v1

High Resolution Settlement Layer (HRSL) Facebook Connectivity Lab and Center for International Earth Science Information Network (CIESIN) Columbia University https://ciesin.columbia.edu/data/hrsl

Global Administrative Unit Layers 2015, Country Boundaries FAO UN - Food and Agriculture Organization of the United Nations https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level0

GHS-SMOD R2016A - GHS settlement grid, following the REGIO model 2014 in application to GHSL Landsat and CIESIN GPW v4-multitemporal (1975-1990-2000-2015) M. Pesaresi and S. Freire http://data.europa.eu/89h/jrc-ghsl-ghs_smod_pop_globe_r2016a

Gridded Population of the World, Version 4 (GPWv4): Population Count CIESIN - Center for International Earth Science Information Network, Columbia University https://doi.org/10.7927/H4X63JVC

Gridded Population of the World, Version 4 (GPWv4): Population Density CIESIN - Center for International Earth Science Information Network, Columbia University https://doi.org/10.7927/H4NP22DQ

GHS-POP R2019A - GHS population grid multitemporal (1975-1990-2000-2015) M. Schiavina, S. Freire, and K. Macmanus https://doi.org/10.2905/0C6B9751-A71F-4062-830B-43C9F432370F

World Development Indicators - Population, total The World Bank https://data.worldbank.org/indicator/SP.POP.TOTL

World development indicators - GNI per capita, Atlas method The World Bank http://data.worldbank.org/indicator/NY.GNP.PCAP.CD

Short summary

Riverine flood risk assessments require the identification of areas prone to potential flooding. We find that (topography-based) hydrogeomorphic floodplain maps can in many cases be useful for riverine flood risk assessments, particularly where hydrologic data are scarce. For 26 countries across the global south, we also demonstrate how dataset choice influences the estimated number of people living within flood-prone zones.