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Abstract. River ice is a common occurrence in cold climate hydrological systems. The annual cycle of river ice formation, growth, decay and clearance can include low flows and ice jams, as well as mid-winter and spring break-up events. Reports and associated data on river ice occurrence are often limited to site and season-specific studies. Within Canada, the National Hydrometric Program (NHP) operates a network of gauging stations with water level as the primary measured variable to derive discharge. In the late 1990s, the Water Science and Technology Directorate of Environment and Climate Change Canada initiated a long-term effort to compile, archive and extract river ice related information from NHP hydrometric records. This data article describes the original research data set produced by this near 20-year effort: the Canadian River Ice Database (CRID). The CRID holds almost 73,000 variables from a network of 196 NHP stations throughout Canada that were in operation within the period 1894 to 2015. Over 100,000 paper and digital files were reviewed representing 10,378 station-years of active operation. The task of compiling this database involved manual extraction and input of more than 460,000 data entries on water level, discharge, date, time and data quality rating. Guidelines on the data extraction, rating procedure and challenges are provided. At each location, a time series of up to 15 variables specific to the occurrence of freeze-up and winter-low events, mid-winter break-up, ice thickness, spring break-up and maximum open-water level were compiled. This database follows up on several earlier efforts to compile information on river ice, which are summarized herein, and expands the scope and detail for use in Canadian river ice research and applications. Following the Government of Canada Open Data initiative, this original river ice data set is available at: https://doi.org/10.18164/c21e1852-ba8e-44af-bc13-48eeedfcf2f4 (de Rham et al., 2020).

Floods, intensified by climate change, pose major challenges for flood zone management in Quebec. This report addresses these issues through two complementary aspects: a historical analysis of the evolution of flood zone management in Quebec and the projected impact of the cartographic and regulatory overhaul, as well as an exploration of the imaginary surrounding the flood-prone territory of the city of Lachute, which has faced recurrent floods for decades and yet continues to be inhabited. The historical analysis reveals that the major floods of 1974, 1976, 2017, and 2019 marked significant turning points in Quebec’s risk management, particularly by highlighting gaps in the regulatory framework and flood zone mapping. The adoption of the Act Respecting Land Use Planning and Development (LAU) in 1979 and the Policy for the Protection of Shorelines, Littorals, and Floodplains (PPRLPI) in 1987 represented a shift toward a preventive approach. However, inconsistencies, insufficient updates to maps, and uneven enforcement of standards have hindered their effectiveness. The catastrophic floods of 2017 and 2019 triggered a regulatory overhaul, a modernization of mapping, and measures to strengthen community resilience. In 2022, a transitional regime came into effect to tighten the regulation of activities in flood zones, pending the adoption of a risk-based management framework. However, to this day, the regulatory perimeters proposed in the modernization project fail to account for the adaptive capacities deployed by communities to live with water, thus providing a biased interpretation of flood risk. The second part explores the social and cultural representations associated with Lachute’s flood-prone territory. It highlights the complex relationships that have developed between residents and the Rivière du Nord through successive flooding episodes and the adaptation strategies implemented to cope, particularly by those who have repeatedly experienced flooding. These residents have come to live with overflow events and to (co)exist with water, challenging the persistent notion that flood-prone areas are inherently dangerous. While local strategies are sometimes innovative, they remain constrained by a regulatory framework that disregards the human experience of the territory and the specific ways in which people inhabit exposed areas to learn to manage flood risks. In summary, this report underscores the urgency of a territorialized, risk-based approach to modernizing flood zone management. It also highlights the need to look beyond cartographic boundaries and better integrate human and cultural dimensions into planning policies, as illustrated in the case of Lachute, to more accurately reflect the true level of risk. These reflections aim to promote more coherent, sustainable, and acceptable management, planning, and development of exposed territories in response to the growing challenges posed by climate change.

UNDRR report published to mark the International Day for Disaster Risk Reduction on October 13, 2020, confirms how extreme weather events have come to dominate the disaster landscape in the 21st century.

Abstract. Groundwater contribution to river flows, generally called base flows, often accounts for a significant proportion of total flow rate, especially during the dry season. The objective of this work is to test simple approaches requiring limited data to understand groundwater contribution to river flows. The Noire river basin in southern Quebec is used as a case study. A lumped conceptual hydrological model (the MOHYSE model), a groundwater flow model (MODFLOW) and hydrograph separation are used to provide estimates of base flow for the study area. Results show that the methods are complementary. Hydrograph separation and the MOHYSE surface flow model provide similar annual estimates for the groundwater contribution to river flow, but monthly base flows can vary significantly between the two methods. Both methods have the advantage of being easily implemented. However, the distinction between aquifer contribution and shallow subsurface contribution to base flow can only be made with a groundwater flow model. The aquifer renewal rate estimated with the MODFLOW model for the Noire River is 30% of the recharge estimated from base flow values. This is a significantly difference which can be crucial for regional-scale water management.

<p>The applicability of the Canadian Precipitation Analysis products known as the Regional Deterministic Precipitation Analysis (CaPA-RDPA) for hydrological modelling in boreal watersheds in Canada, which are constrained with shortage of precipitation information, has been the subject of a number of recent studies. The northern and mid-cordilleran alpine, sub-alpine, and boreal watersheds in Yukon, Canada, are prime examples of such Nordic regions where any hydrological modelling application is greatly scrambled due to lack of accurate precipitation information. In the course of the past few years, proper advancements were tailored to resolve these challenges and a forecasting system was designed at the operational level for short- to medium-range flow and inflow forecasting in major watersheds of interest to Yukon Energy. This forecasting system merges the precipitation products from the North American Ensemble forecasting System (NAEFS) and recorded flows or reconstructed reservoir inflows into the HYDROTEL distributed hydrological model, using the Ensemble Kalman Filtering (EnKF) data assimilation technique. In order to alleviate the adverse effects of scarce precipitation information, the forecasting system also enjoys a snow data assimilation routine in which simulated snowpack water content is updated through a distributed snow correction scheme. Together, both data assimilation schemes offer the system with a framework to accurately estimate flow magnitudes. This robust system not only mitigates the adverse effects of meteorological data constrains in Yukon, but also offers an opportunity to investigate the hydrological footprint of CaPA-RDPA products in Yukon, which is exactly the motivation behind this presentation. However, our overall goal is much more comprehensive as we are trying to elucidate whether assimilating snow monitoring information in a distributed hydrological model could meet the flow estimation accuracy in sparsely gauged basins to the same extent that would be achieved through either (i) the application of precipitation analysis products, or (ii) expanding the meteorological network. A proper answer to this question would provide us with valuable information with respect to the robustness of the snow data assimilation routine in HYDROTEL and the intrinsic added-value of using CaPA-RDPA products in sparsely gauged basins of Yukon.</p>

The following errata have been identified and approved in accordance with the IPCC protocol for addressing possible errors in IPCC assessment reports, synthesis reports and methodology reports as adopted by the Panel at the Thirty-Third Session (Abu Dhabi, 10-13 May 2011) and amended at the Thirty-Seventh Session (Batumi 14-18 October 2013). Errata identified following the approval and acceptance of the Special Report on Climate Change and Land (SRCCL) and prior to publication have been corrected in the final copyedited and laid out draft of the report. Note that page and line numbers for the SPM are based on the numbering used in the revised final draft as distributed Governments st 2019; and line numbers for the underlying chapters are based on the numbering used in the final draft as distributed to Governments on 24 th June 2019.

The Canada Centre for Mapping and Earth Observation (CCMEO) uses Radarsat Constellation Mission (RCM) data for near-real time flood mapping. One of the many advantages of using SAR sensors, is that they are less affected by the cloud coverage and atmospheric conditions, compared to optical sensors. RCM has been used operationally since 2020 and employs 3 satellites, enabling lower revisit times and increased imagery coverage. The team responsible for the production of flood maps in the context of emergency response are able to produce maps within four hours from the data acquisition. Although the results from their automated system are good, there are some limitations to it, requiring manual intervention to correct the data before publication. Main limitations are located in urban and vegetated areas. Work started in 2021 to make use of deep learning algorithms, namely convolutional neural networks (CNN), to improve the performances of the automated production of flood inundation maps. The training dataset make use of the former maps created by the emergency response team and is comprised of over 80 SAR images and corresponding digital elevation model (DEM) in multiple locations in Canada. The training and test images were split in smaller tiles of 256 x 256 pixels, for a total of 22,469 training tiles and 6,821 test tiles. Current implementation uses a U-Net architecture from NRCan geo-deep-learning pipeline (https://github.com/NRCan/geo-deep-learning). To measure performance of the model, intersection over union (IoU) metric is used. The model can achieve 83% IoU for extracting water and flood from background areas over the test tiles. Next steps include increasing the number of different geographical contexts in the training set, towards the integration of the model into production.