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Flood Susceptibility Modeling on Hexagonal Grid Meshes

Recently, researchers have extended conventional hydrological algorithms into a hexagonal grid and noted that hydrological modeling on a hexagonal mesh grid outperformed that on a rectangular grid. This project aimed to apply hexagonal Discrete Global Grid Systems (DGGS) in flood modeling in southern New Brunswick, Canada, where seasonal flooding takes place around the St. John River. Four categories of 28 predictor variables were quantized and included in the machine learning modeling at multiple granularities: geomorphology, hydrography, meteorology, and terrain-derived variables. Results showed that Digital Terrain Model (DTM) was the most important predictor, generally followed by other hydro-geomorphological variables such as distance to water bodies, landcover classifications, and geology types. Meteorology variables, precipitation and total snow, in particular, were listed as important predictors when being added to the models. It meant that total precipitation and snow had a strong impact on the occurrence of flooding in southern New Brunswick. Generally, models had better performance at finer resolutions. Cell-based flooding events were predicted and visualized at three resolution levels. Although there were slight differences in the visualized flooding extent in various scenarios, predicted flooding sites were clustered around the St. John River and its branches.

Key takeaways

1. This project modeled flood susceptibility in hexagonal DGGS.
2. DGGS helped to integrate multi-source data and conduct cell-based predictions.
3. DTM was the most important predictor variable.
4. Meteorology variables showed high importance.
5. Model performance was generally better at finer resolutions.
6. Flood susceptibility was predicted and visualized in a cell-based fashion.

A story map at ArcGIS Online can be found at the link: .

This work has been announced as the first runner-up award in the 2022 Esri Canada Young Scholars competition.