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TitleMulti-scale flood mapping under climate change scenarios in hexagonal discrete global grids
AuthorLi, M; McGrath, HORCID logo; Stefanakis, E
SourceISPRS International Journal of Geo-Information vol. 11, issue 12, 2022 p. 1-26, Open Access logo Open Access
Alt SeriesNatural Resources Canada, Contribution Series 20220363
Mediapaper; digital; on-line
File formatpdf
ProvinceNew Brunswick
Lat/Long WENS -67.0000 -66.0000 46.0000 45.0000
Subjectsenvironmental geology; floods; flood plains; modelling; grid sampling; climate; climate effects; Climate change; machine learning; Geographic information systems; Geology
Illustrationstables; satellite imagery; graphs; flow diagrams; histograms; location maps
ProgramGeobase 2.0 High Resolution Data Exploitation
Released2022 12 17
AbstractAmong the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response and prevention. Machine learning is widely used in flood modeling due to its high performance and scalability. Nonetheless, data pre-processing of heterogeneous sources can be cumbersome, and traditional data processing and modeling have been limited to a single resolution. This study employed an Icosahedral Snyder Equal Area Aperture 3 Hexagonal Discrete Global Grid System (ISEA3H DGGS) as a scalable, standard spatial framework for computation, integration, and analysis of multi-source geospatial data. We managed to incorporate external machine learning algorithms with a DGGS-based data framework, and project future flood risks under multiple climate change scenarios for southern New Brunswick, Canada. A total of 32 explanatory factors including topographical, hydrological, geomorphic, meteorological, and anthropogenic were investigated. Results showed that low elevation and proximity to permanent waterbodies were primary factors of flooding events, and rising spring temperatures can increase flood risk. Flooding extent was predicted to occupy 135-203% of the 2019 flood area, one of the most recent major flooding events, by the year 2100. Our results assisted in understanding the potential impact of climate change on flood risk, and indicated the feasibility of DGGS as the standard data fabric for heterogeneous data integration and incorporated in multi-scale data mining.
Summary(Plain Language Summary, not published)
Flood susceptibility mapping under future climate scenarios in a DGGS, exploring multi-scale data.

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