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The science behind flood mapping

Science and research help make accurate flood maps. As new data are collected and methods of creating different types of maps evolve, so does the science.

Explore ongoing and completed flood mapping research projects led by the Government of Canada.

Research at Natural Resources Canada

Below are some research projects led by Natural Resources Canada:

Watershed uncertainty and delineation

How big is a watershed? Using digital terrain models, we can create a digital representation of any watershed, from the smallest stream catchments to the largest river basins. Researchers at Natural Resources Canada are analyzing watershed delineation maps and outlet points for uncertainties based on underlying errors in the digital terrain model. This information can improve environmental planning in and around sensitive watershed areas.

Artificial Intelligence and Feature Extraction

Analyzing high-resolution satellite images is time-consuming and computationally challenging. Researchers at Natural Resources Canada are using machine learning techniques to train an algorithm to efficiently process a large number of images. The software can automatically recognize and pinpoint the location of important features like buildings, lakes, rivers, forests, and roads. These data can then be used into flood models to measure the impact of flooding.

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CanFlood is an open source flood risk calculation toolkit designed for Canadians developed by Natural Resources Canada. Use it to assess the flood risk of your own home or business.

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Height Above Nearest Drainage and on-the-fly Flood Mapping

Traditional flood maps require large amounts of data to complete complex flow calculations. For many parts of Canada, these data are not available. Natural Resources Canada researchers developed a simplified flood model covering the entire country, which only requires topographic data of a watershed and the shape and depth of the river network. By calculating the height difference between the land grid and the river grid, the Height Above Nearest Drainage (HAND) model produced accurate results in only a fraction of the processing time. This paves the way to complete on-the-fly flood maps that can be used to assist first responders during a flood emergency in any region.

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Impact of Meteorological Variables on Machine Learning Flood Susceptibility Mapping

Identification of flood-prone areas is one of the key steps outlined by the Federal Flood Mapping Guidelines Series. Current research in flood susceptibility mapping uses Machine Learning (ML) algorithms to train computers to identify flood-prone areas based on other available data. So far, few algorithms have fully incorporated meteorological data, even though it is a crucial physical factor in flood events.

Natural Resources Canada researchers ran their ML model on five study areas across Canada and found that meteorological variables improve the accuracy of flood susceptibility mapping. Moreover, these variables can be more important for obtaining accurate results than the traditional datasets tested previously.

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National Elevation Data Strategy

In October 2023, Natural Resources Canada released over 275,000 km2 of LiDAR derived elevation data on Open Maps to complement the High Resolution Digital Elevation Model (HRDEM) and HRDEM Mosaic products. With these new data, over 36.6 million Canadians –92% of the population—lives within the region covered by the HRDEM and HRDEM Mosaic products. Now, 95 of the country’s 100 largest cities are covered by these two products.

The National Elevation Data Strategy is also developing other products like the Automatically extracted buildings and the LiDAR point clouds.

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Assessment of flood-induced building losses using probabilities

The most common method of assessing building damage due to flooding is by creating a depth-damage curve (DDC). A DDC relates economic losses to flood water depths. However, there are complex factors at play that vary across different buildings, including materials, construction quality, and other flood-related factors that can cause damage aside from depth. Natural Resources Canada researchers used seven DDCs for various building types in Southern Ontario to create a Probabilistic DDC—a curve that can predict expected losses for a region based on flood depth that takes into account variations in the built environment. This step can help end users better understand their flood risk.

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Flood Depth Estimation in Historical Floods

In Canada, data from historical floods are often used to make flood management decisions. However, data on maximum flood depths is not available within Canada. This limits the ability to study historical flooding events and results in less informed flood management decisions. A study by Natural Resources Canada has developed a tool that estimates flood depth using simple elevation data. The Rolling Height Above Nearest Drainage (HAND) Inundation Corrected Depth Estimator (RICorDE) tool provides more accurate flood depth estimates than other algorithms, but has limitations when predicting if an area is dry or wet.

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Integrating different systems for accessing elevation data into the Discrete Global Grid System

There are two different systems for accessing elevation data in Canada: the Canadian Digital Elevation Model and the new High Resolution Digital Elevation Model. It is difficult to combine both datasets into a single analysis. Natural Resources Canada researchers used a model called the Discrete Global Grid System to combine these two datasets into a single, standardized grid. This research brings us closer to a national elevation service across various scales for Canada.

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Research at Environment and Climate Change Canada

Below are some research projects led by Environment and Climate Change Canada:

38-year Hindcast of the Surface and Reanalysis of Rivers

How much snow does this valley usually receive? How unusual is the soil moisture we’re experiencing? How often is this river expect to have a streamflow of a given value?

To answer these questions, we are producing a hindcast of Canada’s surface (Canadian Surface Reanalysis – Land; CaSR-Land) and a reanalysis of major river basins (Canadian Surface Reanalysis – Rivers; CaSR-Rivers) from 1980 through 2017. CaSR-Land and CaSR-Rivers are being produced using state-of-the-art models developed by Environment and Climate Change Canada. They draw on atmospheric information from the Regional Deterministic Reforecast System v2.1.

New Technologies and Methods for Measuring Flows and Water Levels

The Water Survey of Canada (WSC) of Environment and Climate Change Canada has been measuring flows and water levels on Canada’s fresh water systems for over 100 years. This information is used by water resource engineers for several reasons, including making critical water management decisions, calibrating hydraulic models, and predicting where and to what extent flooding may occur for different flood scenarios.

The WSC is committed to ensuring that the best available hydrometric (water level and flow) data is accurate and timely. This is done by testing several new technologies and methods, including the use of real-time station cameras to improve the assessment of surface ice conditions during breakup, the use of non-contact surface velocity methods (radar and cameras) to estimate flows during flood conditions, and the suitability of in-situ under-ice hydroacoustics for improving winter (ice affected) discharge data.

New Regional Lakes Representation in CanRCM

The fifth version of Environment and Climate Change Canada’s regional climate model CanRCM5 is currently being finalized by ECCC Canadian Centre for Climate Modelling and Analysis (CCCma). A major advancement of CanRCM5 over previous versions involves its treatment of lakes, which are essential in dynamic downscaling of climate change and critical for hydrological modelling parameters.

In particular, CanRCM5 employs the Canadian Small Lakes Model for all lakes, as well as a new application of runtime correction of lake surface bias, water temperature and ice fragmentation for the Great Lakes. Improvement of evaporation and precipitation processes is demonstrated particularly over the Great Lakes region.

Improved Regional Model Driving for Future Climate Change

A new empirical real-time bias correction (EBC) has recently been developed and applied to Environment and Climate Change Canada’s global Earth System Model, CanESM. The correction has been shown to improve projections of significant climate change. The use of EBC in CanESM improves the training data for dynamic downscaling by regional climate models (RCMs).

As part of the Flood Hazard Identification and Mapping Program contributions, Canada’s two primary regional climate models, CanRCM5 (ECCC) and CRCM5 (Ouranos), will be used to evaluate the added value of EBC. Since RCM data will drive hydrological models, we will apply EBC over uncorrected RCM data to evaluate EBC’s ability in reducing inter-model spread in future projections of RCM data.

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Spatially-distributed climate data for assessing future flood hazards

Environment and Climate Change Canada is collaborating with the Pacific Climate Impacts Consortium to create historical and future high-resolution (~10 km) gridded datasets of daily and sub-daily meteorological variables. The variables include temperature, precipitation, humidity, and wind speed, as well as estimates of rainfall Intensity-Duration-Frequency (IDF) curves.

These datasets will be available for use in hydrological models and assessments of potential flood hazards across Canada under a future climate.

Canadian Surface Reanalysis (CaSR)

Reanalysis data are computer simulations of past meteorological conditions. Environment and Climate Change Canada’s CaSR version 2.1 dataset provides users with hourly reconstructed historical time series (1980–2018) of 28 meteorological variables over all of North America. The data can be used for flood modelling and to examine extreme weather conditions (e.g., precipitation, temperature, wind speed, etc.).

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Global Deterministic Storm Surge Prediction System (GDSPS)

Environment and Climate Change Canada has recently developed a high-resolution global system (GDSPS) to provide total water level (TWL) forecast for all the Canadian coasts. A particular challenge for research and development is to address important physical processes whilst keeping the system computationally efficient for overall forecasts. To address these challenges, effective methods have been developed to improve or include TWL contributions from tides, storm surges, effects of water density, effects of sea ice and their interactions.

The GDSPS has been used to produce a 65 year (1958-2022) hindcast forced by Atmospheric Reanalysis Fifth Generation (ERA5). It will also be used to build an ensemble system with the introduction of tide perturbation.

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