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In this study future flooding frequencies have been estimated for the Grand River catchment located in south - western Ontario, Canada. Historical and future climatic projections made by fifteen Coupled Model Inter - comparison Project - 3 climate models are bias - corrected and downscaled before they are used to obtain mid - and end of 21 st century streamflow projections. By comparing the future projected and historically observed precipitation and temperature record s it is found that the mean and extreme temperature events will intensify in future across the catchment. The increase is more drastic in the case of extreme events than the mean events. The sign of change in future precipitation is uncertain. Further flow extremes are expected to increase in magnitude and frequency in future across the catchment. The confidence in the projection is more for low return period (<10 years) extreme events than higher return period (10 - 100 years) events. It can be expected that increases in temperature will play a dominant role in increasing the magnitude of low return period flooding events while precipitation seems to play an important role in shaping the high return period events.

Flood events and their associated damages have escalated significantly in the last few decades. To add to the gruesome situation, many reports and studies warn that flood risk would aggravate significantly in future periods due to significant alterations in the climate patterns and socio-economic dynamics. Floodplain mapping is looked upon as a viable option to tackle this global issue as it provides both quantitative and qualitative information on flood dynamics. Moreover, with the increasing availability of global data and enhancement in computational simulations, it has become easier to simlate flooding patterns at large scales. This study deter-mines the usability of publicly available datasets in capturing flood hazards over Canada. Run-off data set from the North American Regional Reanalysis (NARR), along with a few other rele-vant inputs are fed to CaMa-Flood, a robust global hydrodynamic model to generate flooding patterns for 1 in 100 and 1 in 200-yr return period events over Canada . The simulated maps are compared and validated with the existing maps of a few flood-prone regions in Canada, thereby establishing their performance over both regional and country-scale. Later, the simulated flood-plain maps are used in conjunction with property related information at 34 cities (within the top 100 populous cities in Canada) to determine the degree of exposure due to flooding in 1991, 2001, and 2011. The results indicate that around 80 percent of inundated spots belong to high and very-high hazard classes in a 200-yr event, which is roughly 4 percent more than simulated for 100-yr event. NARR derived floodplain maps perform very well while compared over the six flood-prone regions. While analyzing the exposure of properties to flooding, we notice an in-crease in the number during the last three decades, with the maximum rise observed in Toronto, followed by Montreal, and Edmonton. To disseminate the extensive flood-related information, a web-based public tool, Flood Map Viewer (http://www.floodmapviewer.com/) is developed. The development of the tool was motivated by the commitment of the Canadian government to provide $63 M over the next three years for the completion of flood maps for higher-risk areas. The study reaches out to demonstrate how publicly available datasets can be utilized with a lesser degree of uncertainty in representing flooding patterns over large regions. The flood re-lated information derived from the study can be used along with vulnerability for quantifying flood risk, which will help in developing appropriate pathways for resilience building for long-term sustainable benefits.

In recent years, geospatial data (e.g. remote sensing imagery), and other relevant ancillary datasets (e.g. land use land cover, climate conditions) have been utilized through sophisticated algorithms to produce global population datasets. With a handful of such datasets, their performances and skill in flood exposure assessment have not been explored. This study proposes a comprehensive framework to understand the dynamics and differences in population flood exposure over Canada by employing four global population datasets alongside the census data from Statistics Canada as the reference. The flood exposure is quantified based on a set of floodplain maps (for 2015, 1 in 100-yr and 1 in 200-yr event) for Canada derived from the CaMa-Flood global flood model. To obtain further insights at the regional level, the methodology is implemented over six flood-prone River Basins in Canada. We find that about 9% (3.31 million) and 11% (3.90 million) of the Canadian population resides within 1 in 100-yr and 1 in 200-yr floodplains. We notice an excellent performance of WorldPop, and LandScan in most of the cases, which is unaffected by the representation of flood hazard, while Global Human Settlement and Gridded Population of the World showed large deviations. At last, we determined the long-term dynamics of population flood exposure and vulnerability from 2006 to 2019. Through this analysis, we also identify the regions that contain a significantly larger population exposed to floods. The relevant conclusions derived from the study highlight the need for careful selection of population datasets for preventing further amplification of uncertainties in flood risk. We recommend a detailed assessment of the severely exposed regions by including precise ground-level information. The results derived from this study may be useful not only for flood risk management but also contribute to understanding other disaster impacts on human-environment interrelationships. • Five population datasets are considered for quantifying flood exposure over Canada. • WorldPop and LandScan provide the closest estimates when compared with census data. • Skill of population datasets is tested over six flood-prone River Basins of Canada. • Long-term changes in degree of exposure is characterized at census-division level. • Highly exposed divisions are identified for ensuring detailed flood-risk assessment

Abstract With the recent Coupled Model Intercomparison Project Phase 6 (CMIP6), water experts and flood modellers are curious to explore the efficacy of the new and upgraded climate models in representing flood inundation dynamics and how they will be impacted in the future by climate change. In this study, for the first time, we consider the latest group of General Circulation Models (GCMs) from CMIP6 to examine the probable changes in floodplain regimes over Canada. A set of 17 GCMs from Shared Socioeconomic Pathways (SSPs) 4.5 (medium forcing) and 8.5 (high end forcing) common to historical (1980 to 2019), near-future (2021 to 2060), and far-future (2061 to 2100) time-periods are selected. A comprehensive framework consisting of hydrodynamic flood modelling, and statistical experiments are put forward to derive high-resolution Canada-wide floodplain maps for 100 and 200-yr return periods. The changes in floodplain regimes for the future periods are analyzed over drainage basin scale in terms of (i) changes in flood inundation extents, (ii) changes in flood hazards (high and very-high classes), and (iii) changes in flood frequency. Our results show a significant rise (>30%) in flood inundation extents in the future periods; particularly intense over western and eastern regions. The flood hazards are expected to cover ~16% more geographical area of Canada. We also find that large areas in northern and western Canada and a few spots in the eastern parts of Canada will be getting flooded more frequently compared to the historical period. The observations derived from this study are vital for enhancing flood preparedness, optimal land-use planning, and refurbishing both structural and non-structural flood control options for improved resilience. The study instills new knowledge on revamping the existing flood management approaches and adaptation strategies for future protection.

Climate change has induced considerable changes in the dynamics of key hydro-climatic variables across Canada, including floods. In this study, runoff projections made by 21 General Climate Models (GCMs) under four Representative Concentration Pathways (RCPs) are used to generate 25 km resolution streamflow estimates across Canada for historical (1961–2005) and future (2061–2100) time-periods. These estimates are used to calculate future projected changes in flood magnitudes and timings across Canada. Results obtained indicate that flood frequencies in the northernmost regions of Canada, and south-western Ontario can be expected to increase in the future. As an example, the historical 100-year return period events in these regions are expected to become 10–60 year return period events. On the other hand, northern prairies and north-central Ontario can be expected to experience decreases in flooding frequencies in future. The historical 100-year return period flood events in these regions are expected to become 160–200 year return period events in future. Furthermore, prairies, parts of Quebec, Ontario, Nunavut, and Yukon territories can be expected to experience earlier snowmelt-driven floods in the future. The results from this study will help decision-makers to effectively manage and design municipal and civil infrastructure in Canada under a changing climate.

Summary Impacts of global climate change on water resources systems are assessed by downscaling coarse scale climate variables into regional scale hydro-climate variables. In this study, a new multisite statistical downscaling method based on beta regression (BR) is developed for generating synthetic precipitation series, which can preserve temporal and spatial dependence along with other historical statistics. The beta regression based downscaling method includes two main steps: (1) prediction of precipitation states for the study area using classification and regression trees, and (2) generation of precipitation at different stations in the study area conditioned on the precipitation states. Daily precipitation data for 53years from the ANUSPLIN data set is used to predict precipitation states of the study area where predictor variables are extracted from the NCEP/NCAR reanalysis data set for the same interval. The proposed model is applied to downscaling daily precipitation at ten different stations in the Campbell River basin, British Columbia, Canada. Results show that the proposed downscaling model can capture spatial and temporal variability of local precipitation very well at various locations. The performance of the model is compared with a recently developed non-parametric kernel regression based downscaling model. The BR model performs better regarding extrapolation compared to the non-parametric kernel regression model. Future precipitation changes under different GHG (greenhouse gas) emission scenarios also projected with the developed downscaling model that reveals a significant amount of changes in future seasonal precipitation and number of wet days in the river basin.

Abstract Floods are the most frequently occurring natural hazard in Canada. An in‐depth understanding of flood seasonality and its drivers at a national scale is essential. Here, a circular, statistics‐based approach is implemented to understand the seasonality of annual‐maximum floods (streamflow) and to identify their responsible drivers across Canada. Nearly 80% and 70% of flood events were found to occur during spring and summer in eastern and western watersheds across Canada, respectively. Flooding in the eastern and western watersheds was primarily driven by snowmelt and extreme precipitation, respectively. This observation suggests that increases in temperature have led to early spring snowmelt‐induced floods throughout eastern Canada. Our results indicate that precipitation (snowmelt) variability can exert large controls on the magnitude of flood peaks in western (eastern) watersheds in Canada. Further, the nonstationarity of flood peaks is modelled to account for impact of the dynamic behaviour of the identified flood drivers on extreme‐flood magnitude by using a cluster of 74 generalized additive models for location scale and shape models, which can capture both the linear and nonlinear characteristics of flood‐peak changes and can model its dependence on external covariates. Using nonstationary frequency analysis, we find that increasing precipitation and snowmelt magnitudes directly resulted in a significant increase in 50‐year streamflow. Our results highlight an east–west asymmetry in flood seasonality, indicating the existence of a climate signal in flood observations. The understating of flood seasonality and flood responses under the dynamic characteristics of precipitation and snowmelt extremes may facilitate the predictability of such events, which can aid in predicting and managing their impacts.

This paper presents an integrated assessment model for use with climate policy decision making in Canada. The feedback based integrated assessment model ANEMI_CDN represents Canada within the global society-biosphere-climate-economy-energy system. The model uses a system dynamics simulation approach to investigate the impacts of climate change in Canada and policy options for adapting to changing global conditions. The disaggregation techniques allow ANEMI_CDN to show results with various temporal resolutions. Two Canadian policy scenarios are presented as illustrative examples to map policy impacts on key model variables, including population, water-stress, food production, energy consumption, and emissions under changing climate over this century. The main finding is a significant impact of a carbon tax on energy consumption. Two policy scenario simulations provide additional insights to policy makers regarding the choice of adaptation/mitigation options along with their implementation time.

Abstract Study region Canada. Study focus Given the effects of climate change on extreme precipitation, updated Intensity-Duration-Frequency (IDF) relationships have been adopted across Canada. Since the IDFs’ generation is based on the assumption of stationarity, the rainfall statistics information may be unreliable. Recent research is attempting to develop a new methodology to integrate non-stationarity and climate change into IDFs updating process. Up to now, there is no comprehensive evaluation of the IDFs updating procedure at different locations. In this study, we analyzed the combined effect of non-stationarity and climate change on future IDFs at six selected gauging stations across Canada. New hydrological insights for the region A comparison of the updated future IDFs with historical IDFs indicates an intensification of extreme events for all study areas, increasing hazard to them. Sites located in the Northeast coastal region will be the most affected in the future by the extreme precipitation. In addition, there is a clear indication that rare events (100-year return period) will become more frequent (in some cases increase up to 443 % of the water infrastructure risk of failure has been observed). We argue that the above findings (i) offer a new overview of future extreme precipitation across Canada, and (ii) should be considered by the stakeholders with respect to climate change adaptation decisions.

This paper provides an overview of the key processes that generate floods in Canada, and a context for the other papers in this special issue – papers that provide detailed examinations of specific floods and flood-generating processes. The historical context of flooding in Canada is outlined, followed by a summary of regional aspects of floods in Canada and descriptions of the processes that generate floods in these regions, including floods generated by snowmelt, rain-on-snow and rainfall. Some flood processes that are particularly relevant, or which have been less well studied in Canada, are described: groundwater, storm surges, ice-jams and urban flooding. The issue of climate change-related trends in floods in Canada is examined, and suggested research needs regarding flood-generating processes are identified.