Municipalities mostly use hydrologic-hydraulic-based models (i.e., engineering models) to generate 100 year flood hazard maps, defined as the probability of an area to be flooded once in a 100 year return period. In this context, there is an urgent need to develop an approach to harmonize and update the existing flood hazard maps and generate a spatially continuous map across the watershed so that localities can best prepare flood mitigation plans. These spatial gaps, inconsistencies and lack of standardization between the provinces are making flood mitigation planning more challenging. These fragmented maps are also largely outdated with many being created or last updated in the 1980s. For example, around the capital region, many existing flood hazard maps, generated during the Flood Damage Reduction Program (FDRP), show discontinuity along the Ottawa River shoreline and between the two provinces ( Figure 2A). This is particularly true for the Ottawa River watershed, where the Ottawa River serves as a natural border between Quebec and Ontario ( Figure 1) for hundreds of kilometers. However, since flood hazard mapping is completed at the provincial level and is often conducted by different municipalities at different times and by different companies, there is a lack of consistency between flood hazard maps. Harmonized and spatially continuous flood hazard maps are key tools to enhance the resilience and sustainability of infrastructures and ecosystems by adapting land use plans, and improving emergency response and flood mitigation risks planning. Consequently, it is critical to identify flood susceptible areas, defined as the potential flood zones in the watershed. These record breaking floods have caused significant socioeconomic, ecotoxicological and ecosystem damage, particularly in the capital region. These flood events occurred in spite of the Ottawa River’s integrated river discharge control system, including 50 major dams and 13 principal reservoirs.

These factors have likely contributed to the record breaking spring flood event in 20 across the Ottawa River watershed. Since 1948, the provinces of Ontario and Quebec have experienced an increase in winter precipitation accompanied by a warmer and wetter spring. In Canada, flood is the costliest natural hazard and with Canada’s accelerated warming, it is predicted that more frequent and intense floods will occur in the next decades. Floods can also transport and mobilize environmentally persistent pollutants including metals stored in alluvial sediments and cause severe water pollution. Floods threaten human societies, infrastructures, natural ecosystems and wildlife. With urbanization and climate change, the world is experiencing an increase in costly and destructive flooding events. Our modeling approach is transferable to harmonize flood maps and fill in spatial gaps in other regions of the world and will help mitigate flood disasters by providing accurate flood data for urban planning.

The machine learning approach and flood classification optimization method developed in this study is also a first step toward Natural Resources Canada’s aim of creating a spatially continuous flood susceptibility map across the Ottawa River watershed. The resulted harmonized and spatially continuous flood susceptibility map has wide-reaching relevance for flood mitigation planning in the capital region. However, caution must be taken in selecting suitable flood conditioning factors and extrapolating classification to areas with similar characteristics to the training sites. We argue that the machine learning classification approach is a promising technique to fill in the spatial gaps between existing flood hazard maps and create harmonized high-resolution flood susceptibility maps across flood-vulnerable areas.

When validated against recently produced 100 year flood hazard maps across the capital region, we find that this random forest classification approach yields a highly accurate flood susceptibility map. We then applied this classification across the Capital Region to fill in the spatial gaps between existing flood hazard maps and generate a harmonized high-resolution (1 m) 100 year flood susceptibility map. We used existing fragmented flood hazard maps along the Ottawa River to train a random forest classification model using a range of flood conditioning factors.

To achieve this goal, we aim to develop a novel approach using machine learning classification (i.e., random forest). With the record breaking flood experienced in Canada’s capital region in 20, there is an urgent need to update and harmonize existing flood hazard maps and fill in the spatial gaps between them to improve flood mitigation strategies.

Eastward ign review