Understanding dynamics of population flood exposure in Canada with multiple high-resolution population datasets

Mohit P Mohanty; Slobodan P Simonovic

doi:10.1016/j.scitotenv.2020.143559

Understanding dynamics of population flood exposure in Canada with multiple high-resolution population datasets

Sci Total Environ. 2021 Mar 10:759:143559. doi: 10.1016/j.scitotenv.2020.143559. Epub 2020 Nov 6.

Authors

Mohit P Mohanty¹, Slobodan P Simonovic²

Affiliations

¹ Department of Civil and Environmental Engineering, The University of Western Ontario, London, Ontario N6A3K7, Canada. Electronic address: mmohanty@uwo.ca.
² Department of Civil and Environmental Engineering, The University of Western Ontario, London, Ontario N6A3K7, Canada.

PMID: 33220996
DOI: 10.1016/j.scitotenv.2020.143559

Abstract

In recent years, geospatial data (e.g. remote sensing imagery), and other relevant ancillary datasets (e.g. land use land cover, climate conditions) have been utilized through sophisticated algorithms to produce global population datasets. With a handful of such datasets, their performances and skill in flood exposure assessment have not been explored. This study proposes a comprehensive framework to understand the dynamics and differences in population flood exposure over Canada by employing four global population datasets alongside the census data from Statistics Canada as the reference. The flood exposure is quantified based on a set of floodplain maps (for 2015, 1 in 100-yr and 1 in 200-yr event) for Canada derived from the CaMa-Flood global flood model. To obtain further insights at the regional level, the methodology is implemented over six flood-prone River Basins in Canada. We find that about 9% (3.31 million) and 11% (3.90 million) of the Canadian population resides within 1 in 100-yr and 1 in 200-yr floodplains. We notice an excellent performance of WorldPop, and LandScan in most of the cases, which is unaffected by the representation of flood hazard, while Global Human Settlement and Gridded Population of the World showed large deviations. At last, we determined the long-term dynamics of population flood exposure and vulnerability from 2006 to 2019. Through this analysis, we also identify the regions that contain a significantly larger population exposed to floods. The relevant conclusions derived from the study highlight the need for careful selection of population datasets for preventing further amplification of uncertainties in flood risk. We recommend a detailed assessment of the severely exposed regions by including precise ground-level information. The results derived from this study may be useful not only for flood risk management but also contribute to understanding other disaster impacts on human-environment interrelationships.

Keywords: Flood exposure; Flood hazard; Flood risk management; Global population datasets; Hydrodynamic flood modeling; Vulnerability.