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<p>In snow-prone regions, snowmelt is one of the main drivers of runoff. For operational flood forecasting and mitigation, the spatial distribution of snow water equivalent (SWE) in near real time is necessary. In this context, in situ observations of SWE provide a valuable information. Nonetheless, the high spatial variability of snowpack characteristics makes it necessary to implement some kind of snow modelling to get a spatially continuous estimation. Data assimilation is thus a useful approach to combine information from both observation and modeling in near real-time. </p><p>For example, at the provincial government of Quebec (eastern Canada), the HYDROTEL Snowpack Model is applied on a daily basis over a 0.1 degree resolution mesh covering the whole province. The modelled SWE is corrected in real time by in situ manual snow survey which are assimilated using a spatial particles filter (Cantet et al., 2019). This assimilation method improves the reliability of SWE estimation at ungauged sites.</p><p>The availability of manual snow surveys is however limited both in space and time. These measurements are conducted on a bi-weekly basis in a limited number of sites. In order to further improve the temporal and spatial observation coverage, alternative sources of data should be considered.</p><p>In this research, it is hypothesized that data gathered by SR50 sonic sensors can be assimilated in the spatial particle filter to improve the SWE estimation. These automatic sensors provide hourly measurements of snow depth and have been deployed in Quebec since 2005. Beforehand, probabilistic SWE estimations were derived from the SR50 snow depth measurements using an ensemble of artificial neural networks (Odry et al. 2019). Considering the nature of the data and the conversion process, the uncertainty associated with this dataset is supposed larger than for the manual snow surveys. The objective of the research is to evaluate the potential interest of adding this lower-quality information in the assimilation framework.</p><p>The addition of frequent but uncertain data in the spatial particle filter required some adjustments in term of assimilation frequency and particle resampling. A reordering of the particles was implemented to maintain the spatial coherence between the different particles. With these changes, the consideration of both manual snow surveys and SR50 data in the spatial particle filter reached performances that are comparable to the initial particle filter that combines only the model and manual snow survey for estimating SWE in ungauged sites. However, the addition of SR50 data in the particle filter allows for continuous information in time, between manual snow surveys.</p><p> </p><p><strong>References:</strong></p><p>Cantet, P., Boucher, M.-A., Lachance-Coutier, S., Turcotte, R., Fortin, V. (2019). Using a particle filter to estimate the spatial distribution of the snowpack water equivalent. J. Hydrometeorol, 20.</p><p>Odry, J., Boucher, M.-A., Cantet,P., Lachance-Cloutier, S., Turcotte, R., St-Louis, P.-Y. (2019). Using artificial neural networks to estimate snow water equivalent from snow depth. Canadian water ressources journal (under review)</p>

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).

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.

<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 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.

The 2019 Global Assessment Report on Disaster Risk Reduction (GAR) is informed by the latest data – including Sendai Framework target reporting by countries using the Sendai Framework Monitor

Among natural-disaster risks, heat waves are responsible for a large number of deaths, diseases and economic losses around the world. As they will increase in severity, duration and frequency over the decades to come within the context of climate change, these extreme events constitute a genuine danger to human health, and heat-warning systems are strongly recommended by public health authorities to reduce this risk of diseases and of excessive mortality and morbidity. Thus, evidence-based public alerting criteria are needed to reduce impacts on human health before and during persistent hot weather conditions. The goal of this guide is to identify alert thresholds for heat waves in Canada based on evidence, and to propose an approach for better defining heat waves in the Canadian context in order to reduce the risks to human health and contribute to the well-being of Canadians. This guide is the result of the collaboration among various research and public institutions working on: 1) meteorological and climate aspects, i.e. the Meteorological Service of Canada (MSC, Environment and Climate Change Canada), and the ESCER centre at the Universite du Quebec a Montreal, and 2) public health, i.e. Health Canada and the Institut National de Sante Publique du Quebec.

Semantic Scholar extracted view of "CLIMATE VARIABILITY AND CHANGE IN CANADA" by E. Barrow et al.

Les bassins versants du Moyen‐Nord quebecois (49e au 55e parallele) se distinguent par leur climatologie et le pourcentage eleve de territoires couverts par des lacs et milieux humides (de l’ordre de 20 a 30 %) et, surtout, par leur importante contribution a la production electrique du Quebec; le complexe de la riviere La Grande generant environ 40% de l’electricite quebecoise. Dans le contexte de la gestion de la production d’electricite, Hydro‐Quebec Production fait la prevision des apports aux reservoirs de ce complexe a l’aide d’un modele hydrologique global. Par ailleurs, depuis les annees 1980, le milieu boreal quebecois a subi des hausses de temperature et de precipitation qui ont modifie le regime des apports aux reservoirs. Compte tenu de ces changements et des caracteristiques physiographiques des bassins boreaux, il a ete propose d’utiliser un modele hydrologique distribue a base physique pour examiner l’impact sur ces apports des projections climatiques produites par Ouranos. En l’occurrence le modele HYDROTEL dont la prise en mains est en train d’etre completee par Hydro‐Quebec Production. Le modele qui est maintenant convenablement cale pour un certain nombre de bassins repond aux attentes dans les bassins du sud du Quebec. Toutefois, pour les grands bassins du Nord comme ceux du Complexe La Grande, l’utilisation du modele requiert des travaux d’adaptations, entre autres, aux niveaux de la modelisation des milieux humides et de la desagregation spatiale des precipitations simulees par les modeles climatiques. Les objectifs generaux de ce projet etaient d’accroitre notre comprehension de l’hydrologie du moyen nord afin qu’elle soit bien representee dans HYDROTEL tout en tenant compte des incertitudes parametriques associees aux differentes equations gouvernant les processus physiques. Ces objectives ont ete declines en trois activites de travail : (AT1) modelisation des processus hydrologiques; (AT2) calage et analyses de sensibilite, d’identifiabilite et d’incertitudes des parametres de calage d’HYDROTEL; et (AT3) amelioration des plateformes informatiques HYDROTEL et PHYSITEL, ce dernier etant un SIG dedie a la construction des bases de donnees de modeles hydrologiques distribues. Pour Ouranos et Hydro‐Quebec les principales realisations issues de ce projet incluent : (i) le developpement d’une methode eprouvee de desagregation sous grille de la precipitation mesoechelle permettant d’evaluer a fine echelle spatiale l’impact des changements climatiques sur les precipitations; (ii) une meilleure comprehension de la dynamique des ecoulements, du stockage de l’eau et de l’evapotranspiration d’un petit bassin versant boreal incluant une grande une tourbiere minerotrophe aqualysee; (iii) l’evaluation du parametrage de la sublimation et la relocalisation de la neige dues au vent et l’identification du besoin d’inclure le rayonnement sous la canopee pour bien reproduire la crue avec un modele complexe de l'evolution du couvert nival; (iv) la detection de la quasi neutralite frequente (~76% du temps, majoritairement le jour) de l’atmosphere au‐dessus d’un milieu humide causee par une turbulence mecanique forte et une grande inertie thermique; conditions ayant permises le developpement d’un modele simple d’evapotranspiration des milieux humides base le transfert massique et la stabilite atmospherique; (v) le developpement d’un modele de rayonnement net base uniquement sur des donnees de temperatures journalieres (min, max) et une estimation des parametres permettant de valider l’utilisation de l’equation de Penman‐Monteith dans le nord quebecois; (vi) la hierarchisation des parametres de calage d’HYDROTEL selon la saison et le developpement d’une methode permettant d’evaluer l’incertitude sur les debits simules et d’identifier son importance durant la fonte et l’etiage estival; (vii) dans un contexte d’analyse frequentielle des debits simules, evaluation de l’incertitude parametrique par rapport a l’incertitude statistique, cette derniere dominant pour les periodes de retour superieures a cinq ans; (viii) a l’aide de PHYSITEL, la premiere discretisation du complexe de la riviere La Grande (136 648 km2) en six sousbassins (LG1, LG2, LG3, LG4, La Forge 1 & 2,et Caniapiscau) leur subdivision en versants permettant le calcul de crues maximales probables a l’aide d’HYDROTEL; et (ix) le developpement d’une version 64 bits d’HYDROTEL incluant de nouveaux modules de de calculs de la temperature du sol et des bilans hydriques des milieux humides et isoles. L'avancement de nos comprehensions de l'hydrologie des milieux humides et du milieu boreal en general a ete a la base du developpement des versions adaptees d'HYDROTEL et de PHYSITEL qui permettront a Hydro‐Quebec d'apprehender, avec une modelisation distribuee, l'impact des changements climatiques sur le complexe de la riviere La Grande. Ces logiciels sont transposables a l’ensemble du milieu boreal canadien. Une entente conclut, depuis 2005, entre l’INRS et Hydro‐Quebec (HQ) permet d’ailleurs une distribution commerciale des differentes versions d’HYDROTEL avec interfaces usagers de meme qu’une distribution communautaire du noyau de calcul. Cette synergie a permis de mettre en commun des ressources et des expertises qui facilitent les echanges scientifiques et techniques entre les concepteurs d’HYDROTEL, le Centre d’expertise hydrique du Quebec (CEHQ), HQ, l’IREQ (Institut de recherche en electricite du Quebec) et d’autres usagers (ex. : l’IMTA, Instituto Mexicano de Technologia del Agua). Au total, plus d’une quarantaine de licences ont ete distribuees tant pour des besoins d’enseignement (Universite de Sherbrooke) et de recherche (Universite Laval, UQTR, UQAC, IREQ, Ecole de Technologie Superieure, INRA de Montpellier, Environnement Canada, Agriculture et Agroalimentaire Canada), que des besoins de prevision hydrologique (IMTA, Ville de Quebec, Centre d’expertise hydrique du Quebec, HQ). La modularite informatique d’HYDROTEL se prete egalement bien a cette synergie car elle offre la possibilite de partager le savoir‐faire et, par l’entremise d’un site internet public (CodePlex), de mettre a la disponibilite de tous les nouvelles versions du noyau de calcul. Ces developpements ont permis a l’equipe de l’INRS‐ETE d’acquerir une reconnaissance internationale en modelisation hydrologique distribuee. En effet, HYDROTEL et PHYSITEL ont dans le passe ete identifie comme les outils a utiliser dans le cadre d’appels de proposition de projets de determination du potentiel hydroelectrique finances par la Banque Mondiale [World Bank, 2009].

Cette stratégie oriente les activités scientifiques d'Environnement et Changement climatique Canada afin de favoriser un avenir plus vert et plus durable. Elle met l'accent sur nos gens, nos valeurs et nos priorités tournées vers l'avenir en tant que ministère fédéral à vocation scientifique.