This series of projected future flood susceptibility maps were generated using an XGBoost machine learning model trained on major floods from 2005 to 2023. The trained model was applied to future climate scenarios for 2050, 2070, and 2100, under two SSP scenarios: 245 and 585. The model uses temperature and precipitation time series to estimate potential future flood susceptibility. These maps represent model projections and should be interpreted as indicators of potential flood susceptibility, not precise forecasts.This dataset forms part of a broader collection of flood susceptibility datasets, offering related information and analyses. The collection includes an overview page with associated publications, historic susceptibility values, temporal trends, and future projections.- [Collection – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc)- [Historic - Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/ea1384df-bf4a-4743-97bb-870dc43f8d77)- [Trends and Extremes – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/3202e0a0-0afb-4120-b102-b0c41f0fb9eb)

This series of historic flood susceptibility maps comes from an XGBboost machine learning model trained on major floods from 2005 to 2023. The trained model is then run for each year from 2000 to 2023, including unique temporal characteristics of temperature, precipitation, land use land cover and Normalized Difference Vegetation Index (NDVI), to predict the flood susceptibility of any given year.This dataset forms part of a broader collection of flood susceptibility datasets, offering related information and analyses. The collection includes an overview page with associated publications, historic susceptibility values, temporal trends, and future projections.- [Collection – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc)- [Trends and Extremes – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/3202e0a0-0afb-4120-b102-b0c41f0fb9eb)- [Future - Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/c00f95a3-7bab-4d28-b9cc-b30f06b5afd2)

This dataset is archived, please refer to: - [Collection – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc) for the new release.This national map of flood susceptibility or flood prone areas is based on patterns of historic flood events as predicted by an ensemble machine learning model.The recommended use is national, provincial or regional scale and can be used as a guide for identifying areas for further investigation. The Flood Susceptibility Index (FSI) Dataset, while processed and available at 30m cell size, is not recommended for use at the pixel or street level, given the uncertainty in the modelling process and the variability of results as discussed in https://www.mdpi.com/2673-4931/25/1/18 .For additional details on the methods, tests, models and datasets used to generate this data layer, please see https://geoscan.nrcan.gc.ca/starweb/geoscan/servlet.starweb?path=geoscan/fulle.web&search1=R=329493

This collection of flood susceptibility products captures temporal patterns of historic flood events from 2000 to 2023, derived from flood susceptibility maps. The layers are designed to support decision-making by providing easily interpretable information for planning, screening, and other management purposes.This collection contains several datasets which explore the suite of rasters from 2000 to 2023:- Extreme wet and extreme dry years and the associated average flood susceptibility, processed by NHN WU, as found by Generalized Extreme Value (GEV) analysis- Flood susceptibility envelope, which is derived from the above wet and dry years- Trend slope, for each pixel the value for each year is extracted and the linear trend is computed, greater than 1000 is increasing flood susceptibility and below 1000 is decreasing flood susceptibility. - Trend Class, as identified by wet and dry scores, applied to NHN WU- Current flood susceptibility, which represents the estimated average value of present-day flood susceptibility- Current interquartile range, which provides and indication of the temporal variability of a given pixel under the current regime.This dataset forms part of a broader collection of flood susceptibility datasets, offering related information and analyses. The collection includes an overview page with associated publications, historic susceptibility values, temporal trends, and future projections.- **[Collection - Flood Susceptibility Mapping](https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc)**- **[Historic - Flood Susceptibility Mapping](https://open.canada.ca/data/en/dataset/ea1384df-bf4a-4743-97bb-870dc43f8d77)**- **[Future - Flood Susceptibility Mapping](https://open.canada.ca/data/en/dataset/c00f95a3-7bab-4d28-b9cc-b30f06b5afd2)**

This collection of flood susceptibility products captures temporal patterns of historic flood events from 2000 to 2023, derived from flood susceptibility maps. The layers are designed to support decision-making by providing easily interpretable information for planning, screening, and other management purposes. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

This collection of flood susceptibility products captures temporal patterns of historic flood events from 2000 to 2023, derived from flood susceptibility maps. The layers are designed to support decision-making by providing easily interpretable information for planning, screening, and other management purposes. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

This series of projected future flood susceptibility maps were generated using an XGBoost machine learning model trained on major floods from 2005 to 2023. The trained model was applied to future climate scenarios for 2050, 2070, and 2100, under two scenarios: 245 and 585. The model uses temperature and precipitation time series to estimate potential future flood susceptibility. These maps represent model projections and should be interpreted as indicators of potential flood susceptibility, not precise forecasts. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

This series of projected future flood susceptibility maps were generated using an XGBoost machine learning model trained on major floods from 2005 to 2023. The trained model was applied to future climate scenarios for 2050, 2070, and 2100, under two scenarios: 245 and 585. The model uses temperature and precipitation time series to estimate potential future flood susceptibility. These maps represent model projections and should be interpreted as indicators of potential flood susceptibility, not precise forecasts. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

This series of projected future flood susceptibility maps were generated using an XGBoost machine learning model trained on major floods from 2005 to 2023. The trained model was applied to future climate scenarios for 2050, 2070, and 2100, under two scenarios: 245 and 585. The model uses temperature and precipitation time series to estimate potential future flood susceptibility. These maps represent model projections and should be interpreted as indicators of potential flood susceptibility, not precise forecasts. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**

This series of projected future flood susceptibility maps were generated using an XGBoost machine learning model trained on major floods from 2005 to 2023. The trained model was applied to future climate scenarios for 2050, 2070, and 2100, under two scenarios: 245 and 585. The model uses temperature and precipitation time series to estimate potential future flood susceptibility. These maps represent model projections and should be interpreted as indicators of potential flood susceptibility, not precise forecasts. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**