Canada’s geography and climate make it particularly vulnerable to natural disasters, with floods being the most frequent and costly. Factors such as heavy rainfall, storm surges, and inadequate drainage systems contribute to these floods, posing significant threats to communities, property, and the environment across the nation.
This study investigates flood distribution and intensity in Canada by analyzing the following datasets:
- Historical Flood Events (HFE)
- Hydrometric Data from the Water Survey of Canada
- National Hydronetwork Dataset
- Gaussian Kernel Density Estimation (KDE) is used to identify flood patterns and contributing factors, particularly focusing on the five most affected provinces.
- Both non-parametric and parametric methods are employed to estimate flood intensity.
- Point pattern analysis and KDE are applied to visualize flood hotspots, providing insights into spatial distribution patterns.
- Explores the impact of hydrometric factors, specifically discharge values, on flood occurrences.
- A detailed analysis of Quebec is conducted by incorporating discharge values and spatial coordinates, offering a comprehensive understanding of flood intensity patterns.
To further enhance disaster preparedness, we predicted peak discharge levels for the years 2023 to 2026, as the current hydrometric dataset includes data only up to 2022. Using Integrated Nested Laplace Approximation (INLA) for forecasting, we estimated the potential timing of floods by analyzing the projected rise in peak discharge levels over the coming years.
This research contributes valuable insights for disaster preparedness and risk mitigation in Canada. By understanding flood distribution and intensity patterns through spatial analysis, policymakers and emergency planners can implement more effective disaster management strategies and better prepare for future flood events.