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With the record breaking flood experienced in Canada’s capital region in 2017 and 2019, 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. To achieve this goal, we aim to develop a novel approach using machine learning classification (i.e., random forest). 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. 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. 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 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. However, caution must be taken in selecting suitable flood conditioning factors and extrapolating classification to areas with similar characteristics to the training sites. The resulted harmonized and spatially continuous flood susceptibility map has wide-reaching relevance for flood mitigation planning in the capital region. 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. 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.

Hydrological responses in cold regions are often complex and variable (both spatially and temporally) due to the complex and multiple interactions between the hydrological processes at play. Thus, there is a need to better understand and represent cold region hydrological processes within hydrological models. In this study, a physicallybased hydrological model has been developed using the Cold Regions Hydrological Model (CRHM) platform for the L’Acadie River Catchment in southern Quebec (Canada). Almost 70 % of the catchment is occupied by agricultural fields, being representative of the intensive farming landscape of the southern St-Lawrence lowlands, while the rest is mostly forested. The physical processes including blowing snow, snow interception in canopies, sublimation and snowmelt were simulated over 35 years using the CRHM platform. Hydrologic response units (HRUs), the smallest simulation spatial unit within the catchment, were derived based on the combination of land use/cover and vegetation types. Over the simulation period, considerable spatial variability was detected between agricultural and forested sites. Snow accumulation and associated snow water equivalent (SWE) were found to be higher in forested sites than agricultural sites, which can be explained by blowing snow transport from agricultural sites to the forested sites where aerodynamic roughness is greater. Higher rates of blowing snow sublimation were detected over the agricultural sites compared to snow intercepted in the forest canopies. This can be explained by the fact that there is a great amount of blowing snow over the agricultural sites, and thus available suspended snow for sublimation, while over the forested sites the snow is more firmly retained by the canopies and thus there is less blowing snow and consequently less blowing snow sublimation. In addition, although snow cover duration shows variation over the simulation period, the snow generally lasts longer in forested fields than in agricultural fields. Our findings indicating more snow in forested fields than agricultural (open) fields are contrary to the usual notion that there is less snow accumulation on forest ground due to the high rates of canopy sublimation. However, this is true for the landscapes dominated by forests, while our study area is dominated by agricultural fields, so snow erosion of agricultural fields and snow deposition in forested fields seem to compensate canopy losses. Taken together, it is shown that land use exerts a critical control on snow distributions in this type of landscape, and perhaps on possible implications for future snow hydrology of the catchment.

Snowmelt dominated regions are receiving increasing attention due to their noticeably rapid response to ongoing climate change, which raises concerns about the altered hydrological risks under climate change scenarios. This study aims to assess the climate change impacts on hydrology over two contrasted catchments in southern Québec: Acadie River and Montmorency River catchments. These river catchments represent two predominant landscapes of the St. Lawrence River watershed; an intensive farming landscape in the south shore lowlands and the forested landscape on the Canadian Shield on the north shore, respectively. In this study, a physically based hydrological model has been developed using the Cold Regions Hydrological Model (CRHM) for both of the catchments. The hydrological model outputs showed that we were able to simulate snow surveys and discharge measurements with a reasonable accuracy for both catchments. The acceptable performance of the model along with the strong physical basis of structure suggested that this model could be used for climate change sensitivity simulations. Based on the climate scenarios reviewed, a temperature increase up to 8°C and an increase in total precipitation up to 20% were analysed for both of the catchments. Both catchments were found to be sensitive to climate change, however the degree of sensitivity was found to be catchment specific. Snow processes in the Acadie River catchment were simulated to be more sensitive to warming than in the Montmorency River catchment. In case of 2°C warming, reduction in peak SWE was not be able to be compensated even by increased precipitation scenario. Given that, the Acadie River has already a mixed flow regime, even if 2°C warming is combined with an increase in precipitation, pluvial regime kept becoming more dominant, resulting in higher peaks of rain events. On the other hand, even 3°C of warming did not modify the flow regime of the Montmorency River. While there is shift towards earlier peak spring flows in both catchments, the shift was found to be more pronounced in the Acadie River. An earlier occurrence of snowmelt floods and an overall increase in winter streamflow during winter have been simulated for both catchments, which calls for renewed assessments of existing water supply and flood risk management strategies.

Abstract This study confronts the new concept of ‘surface storage’ with the old concept of ‘sponge effect’ to explain the spatio-temporal variability of the annual daily maximum flows measured in 17 watersheds of southern Quebec during the period 1930–2019. The new concept takes into account the hydrological impacts of wetlands and other topographic components of the landscape (lakes, depressions, ditches, etc.) while that of the sponge effect only takes into account the hydrological impacts of wetlands. With regard to spatial variability, the area of wetlands and other water bodies is the variable best correlated negatively with the magnitude but positively with the duration of flows. As for the temporal variability, the application of the long-term trend tests revealed a significant increase in the magnitude and, to a lesser extent, the duration of the flows occurring in the watersheds of the north shore characterized by a greater area of wetlands and other water bodies (>5%). This increase is explained by the fact that the storage capacity of these land types, which remains unchanged over time, does not make it possible to store the surplus runoff water brought by the increase in rainfall during the snowmelt season.

Abstract Quebec is experiencing a significant increase in summer and fall temperatures and rainfall. This study compares the spatiotemporal variability of maximum daily flows generated by rainfall during the fall season (September–December) in relation to this climatic change and physiographic and land use factors. Analysis of the spatial variability of these maximum flows measured from 1930 to 2018 in 17 watersheds revealed that the magnitude of flows is approximately twice as low on the north shore as it is on the south shore south of 47° N. This difference is explained by three main factors: wetlands (negative correlation) and agricultural (positive correlation) surface area, and summer–fall total precipitation (positive correlation). As for the temporal variability of flows, the different Mann–Kendall statistical tests showed a significant increase in flows due to increased rainfall. The increase of flows was more widespread on the north shore than on the south because the storage capacity of wetlands and other water bodies does not change over time to store excess rainfall. On the south shore, the increase in flows over time is limited due to the significant reduction in agricultural areas since the modernization of agriculture. This reduction favored infiltration to the detriment of runoff.

Abstract The objective of this study is to compare the spatiotemporal variability of seasonal daily mean flows measured in 17 watersheds, grouped into three homogeneous hydroclimatic regions, during the period 1930–2023 in southern Quebec. With regard to spatial variability, unlike extreme daily flows, seasonal daily mean flows are very poorly correlated with physiographic factors and land use and land cover. In fall, they are not correlated with any physiographic or climatic factor. In winter, they are positively correlated with the rainfall and winter daily mean maximum temperatures. In spring, they are strongly correlated positively with the snowfall but negatively with the spring daily mean maximum temperatures. However, in summer, they are better correlated with forest area and, to a lesser extent, with the rainfall. As for their temporal variability, the application of six different statistical tests revealed a general increase in daily mean flows in winter due to early snowmelt and increased rainfall in fall. In summer, flows decreased significantly in the snowiest hydroclimatic region on the south shore due to the decrease in the snowfall. In spring, no significant change in flows was globally observed in the three hydroclimatic regions despite the decrease in the snowfall due to the increase in the rainfall. In fall, flows increased significantly south of 47°N on both shores due to the increase in the rainfall. This study demonstrates that, unlike extreme flows, the temporal variability of seasonal daily average flows is exclusively influenced by climatic variables in southern Quebec. Due to this influence, seasonal daily mean flows thus appear to be the best indicator for monitoring the impacts of changes in precipitation regimes and seasonal temperatures on river flows in southern Quebec.

Quebec has experienced a significant decrease in the amount of snow and an increase in temperature during the cold season. The objective of this study is to analyze the impacts of these climate changes on the spatio-temporal variability of the daily maximum flows generated by snowmelt in winter and spring using several statistical tests of correlation (spatial variability) and long-term trend (temporal variability). The study is based on the analysis of flows measured in 17 watersheds (1930–2019) grouped into three hydroclimatic regions. Regarding the spatial variability, the correlation analysis revealed that in winter, the flows are positively correlated with the agricultural area and the daily maximum winter temperature. In the spring, the flows are positively correlated with the drainage density and the snowfall but negatively correlated with the area of wetlands and the daily maximum spring temperature. As for temporal variability (long-term trend), the application of eight statistical tests revealed a generalized increase in flows in winter due to early snowmelt. In the spring, despite the decreased snow cover, no negative trend was observed due to the increase in the spring rainfall, which compensates for the decrease in the snowfall. This temporal evolution of flows in the spring does not correspond to the predictions of climate models. These predict a decrease in the magnitude of spring floods due to the decrease in the snowfall in southern Quebec.

Snow is the main source of streamflow in temperate regions characterized by very cold and snowy winters. Due to global warming, these regions are experiencing a significant decrease in snowfall. The main objective of this study is to analyze the impacts of snowfall on the spatio-temporal variability of mean annual flows (MAFs) of 17 rivers, grouped into three hydroclimatic regions, from 1930 to 2019 in southern Quebec. In terms of spatial variability, snowfall is the variable most correlated with MAFs (positive correlation), followed by drainage density (positive correlation) and wetland surface areas (negative correlation). Due to the influence of these three factors, MAF values are generally higher in the most agricultural watersheds of the southeastern hydroclimatic region on the south shore than in the less agricultural watersheds of the southwestern hydroclimatic region on the north shore of the St. Lawrence River. As for temporal variability, the four statistical tests applied to the hydrological series detect no significant downward trend in MAFs, despite having reduced snowfall. Instead, they suggest an evolution toward an increase in mean annual flows, as a result of increased rainfall due to the increase in temperature. This evolution is more pronounced on the north shore than on the south shore, likely due to the presence of wetlands and others water bodies, whose runoff water storage capacity does not change over time to be able to store the surplus of the quantity of water brought by the increase in rain.

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.

Modifications to land can serve to jointly reduce risks of floods and droughts to people and to ecosystems. Whether land modifications are implemented will depend on the willingness and ability of a diversity of actors. This article reviews the state of knowledge on land modification use in areas exposed to dual hydrologic risks and the land owners, managers, and users who directly make decisions about action on lands they control. The review presents a typology of land modifications and explains how land modifications interact with the hydrological cycle to reduce risks. It then addresses the roles and perspectives of the land owners, managers, and users undertaking land modifications, summarizing theories explaining motivations for, as well as barriers to and enablers of, land modification implementation. The analysis reveals geographical differences in narratives on land modifications as well as knowledge gaps regarding variation across actors and types of land modifications.