Understanding Flood Risk Perception: A Case Study from Canada - Oubennaceur, Khalid; Chokmani, Karem; Lessard, Florence; Gauthier, Yves; Baltazar, Catherine; Toussaint, Jean-Patrick (Sustainability, 2022)
In recent years, understanding and improving the perception of flood risk has become an important aspect of flood risk management and flood risk reduction policies. The aim of this study was to explore perceptions of flood risk in the Petite Nation River watershed, located in southern Quebec, Canada. A survey was conducted with 130 residents living on a floodplain in this river watershed, which had been affected by floods in the spring of 2017. Participants were asked about different aspects related to flood risk, such as the flood hazard experience, the physical changes occurring in the environment, climate change, information accessibility, flood risk governance, adaptation measures, and finally the perception of losses. An analysis of these factors provided perspectives for improving flood risk communication and increasing the public awareness of flood risk. The results indicated that the analyzed aspects are potentially important in terms of risk perception and showed that the flood risk perceptions varied for each aspect analyzed. In general, the information regarding flood risk management is available and generally understandable, and the level of confidence was good towards most authorities. However, the experiences of flood risk and the consequences of climate change on floods were not clear among the respondents. Regarding the adaptation measures, the majority of participants tended to consider non-structural adaptation measures as being more relevant than structural ones. Moreover, the long-term consequences of flooding on property values are of highest concern. These results provide a snapshot of citizens’ risk perceptions and their opinions on topics that are directly related to such risks.

Adapting to Rising Flood Risk: An Analysis of Insurance Solutions for Canada - Task Force on Flood Insurance and Relocation (Public Safety Canada, 2022)

The Usefulness of Global and Regional Precipitation and Temperature Reanalyses for Flood Modeling at the Catchment Scale - Valdez, Emixi; Anctil, Francois; Ramos, Maria-Helena (AGU Fall Meeting Abstracts, 2022)
Atmospheric reanalysis data provides a numerical description of global and regional water cycles by combining models and observations. These datasets are increasingly valuable as a substitute for observations in regions where these are scarce. They could significantly contribute to reducing losses by feeding flood early warning systems that can inform the population and guide civil security action. We assessed the suitability of two different precipitation and temperature reanalysis products readily available for predicting historic flooding of the La Chaudière River in Quebec: 1) Environment and Climate Change Canada's Regional Deterministic Reanalysis System (RDRS-v2) and 2) ERA5 from the Copernicus Climate Change Service. We exploited a multi-model hydrological ensemble prediction system that considers three sources of uncertainty: initial conditions, model structure, and weather forcing to produce streamflow forecasts up to 5 days into the future with a time step of 3 hours. These results are compared to a provincial reference product based on gauge measurements of the Ministère de l'Environnement et de la Lutte contre les Changements Climatiques. Then, five conceptual hydrological models were calibrated with three different meteorological datasets (RDRS-v2, ERA5, and observational gridded) and fed with two ensemble weather forecast products: 1) the Regional Ensemble Prediction System (REPS) from the Environment and Climate Change Canada and 2) the ensemble forecast issued by the European Centre for Medium-Range Weather Forecasts (ECMWF). Results reveal that the calibration of the model with reanalysis data as input delivered a higher accuracy in the streamflow simulation providing a useful resource for flood modeling where no other data is available. However, although the selection of the reanalysis is a determinant of capturing the flood volumes, selecting weather forecasts is more critical in anticipating discharge threshold exceedances.

The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) - Mai, Juliane; Shen, Hongren; Tolson, Bryan A.; Gaborit, Étienne; Arsenault, Richard; Craig, James R.; Fortin, Vincent; Fry, Lauren M.; Gauch, Martin; Klotz, Daniel; Kratzert, Frederik; O'Brien, Nicole; et al. (Hydrology and Earth System Sciences, 2022)
Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×106 km2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways – either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows: The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations that the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i.e., HYMOD2-lumped) and regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The machine-learning-based model was not included here as it is not set up to simulate AET, SSM, and SWE. All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download the data and model outputs.

A Hydrograph-Based Approach to Improve Satellite-Derived Snow Water Equivalent at the Watershed Scale - Whittaker, Charles; Leconte, Robert (Water, 2022)
For the past few decades, remote sensing has been a valuable tool for deriving global information on snow water equivalent (SWE), where products derived from space-borne passive microwave radiometers are favoured as they respond to snow depth, an important component of SWE. GlobSnow, a novel SWE product, has increased the accuracy of global-scale SWE estimates by combining remotely sensed radiometric data with other physiographic characteristics, such as snow depth, as quantified by climatic stations. However, research has demonstrated that passive microwaves algorithms tend to underestimate SWE for deep snowpack. Approaches were proposed to correct for such underestimation; however, they are computer intensive and complex to implement at the watershed scale. In this study, SWEmax information from the near real time 5-km GlobSnow product, provided by Copernicus and the European Space Agency (ESA) and GlobSnow product at 25 km resolution were corrected using a simple bias correction approach for watershed scale applications. This method, referred to as the Watershed Scale Correction (WSC) approach, estimates the bias based on the direct runoff that occurs during the spring melt season. Direct runoff is estimated on the one hand from SWEmax information as main input. Infiltration is also considered in computing direct runoff. An independent estimation of direct runoff from gauged stations is also performed. Discrepancy between these estimates allows for estimating the bias correction factor. This approach is advantageous as it exploits data that commonly exists i.e., flow at gauged stations and remotely sensed/reanalysis data such as snow cover and precipitation. The WSC approach was applied to watersheds located in Eastern Canada. It was found that the average bias moved from 33.5% with existing GlobSnow product to 18% with the corrected product, using the recommended recursive filter coefficient β of 0.925 for baseflow separation. Results show the usefulness of integrating direct runoff for bias correction of existing GlobSnow product at the watershed scale. In addition, potential benefits are offered using the recursive filter approach for baseflow separation of watersheds with limited in situ SWE measurements, to further reduce overall uncertainties and bias. The WSC approach should be appealing for poorly monitored watersheds where SWE measurements are critical for hydropower production and where snowmelt can pose serious flood-related damages.
Keywords: Infolettre; Infolettre_Fevrier_2023

Convolutional neural network and long short-term memory models for ice-jam predictions - Madaeni, Fatemehalsadat; Chokmani, Karem; Lhissou, Rachid; Homayouni, Saeid; Gauthier, Yves; Tolszczuk-Leclerc, Simon (The Cryosphere, 2022)
In cold regions, ice jams frequently result in severe flooding due to a rapid rise in water levels upstream of the jam. Sudden floods resulting from ice jams threaten human safety and cause damage to properties and infrastructure. Hence, ice-jam prediction tools can give an early warning to increase response time and minimize the possible damages. However, ice-jam prediction has always been a challenge as there is no analytical method available for this purpose. Nonetheless, ice jams form when some hydro-meteorological conditions happen, a few hours to a few days before the event. Ice-jam prediction can be addressed as a binary multivariate time-series classification. Deep learning techniques have been widely used for time-series classification in many fields such as finance, engineering, weather forecasting, and medicine. In this research, we successfully applied convolutional neural networks (CNN), long short-term memory (LSTM), and combined convolutional–long short-term memory (CNN-LSTM) networks to predict the formation of ice jams in 150 rivers in the province of Quebec (Canada). We also employed machine learning methods including support vector machine (SVM), k-nearest neighbors classifier (KNN), decision tree, and multilayer perceptron (MLP) for this purpose. The hydro-meteorological variables (e.g., temperature, precipitation, and snow depth) along with the corresponding jam or no-jam events are used as model inputs. Ten percent of the data were excluded from the model and set aside for testing, and 100 reshuffling and splitting iterations were applied to 80 % of the remaining data for training and 20 % for validation. The developed deep learning models achieved improvements in performance in comparison to the developed machine learning models. The results show that the CNN-LSTM model yields the best results in the validation and testing with F1 scores of 0.82 and 0.92, respectively. This demonstrates that CNN and LSTM models are complementary, and a combination of both further improves classification.
Keywords: Infolettre; Infolettre_Juillet_2022; Site_internet_RIISQ

Interpolation of Precipitation Extremes on a Large Domain Toward IDF Curve Construction at Unmonitored Locations - Jalbert, Jonathan; Genest, Christian; Perreault, Luc (Journal of Agricultural, Biological and Environmental Statistics, 2022)
Abstract An intensity–duration–frequency (IDF) curve describes the relationship between rainfall intensity and duration for a given return period and location. Such curves are obtained through frequency analysis of rainfall data and commonly used in infrastructure design, flood protection, water management, and urban drainage systems. However, they are typically available only in sparse locations. Data for other sites must be interpolated as the need arises. This paper describes how extreme precipitation of several durations can be interpolated to compute IDF curves on a large, sparse domain. In the absence of local data, a reconstruction of the historical meteorology is used as a covariate for interpolating extreme precipitation characteristics. This covariate is included in a hierarchical Bayesian spatial model for extreme precipitations. This model is especially well suited for a covariate gridded structure, thereby enabling fast and precise computations. As an illustration, the methodology is used to construct IDF curves over Eastern Canada. An extensive cross-validation study shows that at locations where data are available, the proposed method generally improves on the current practice of Environment and Climate Change Canada which relies on a moment-based fit of the Gumbel extreme-value distribution.
Keywords: Infolettre; Infolettre_Avril_2022; Site_internet_RIISQ

Uncertainty and sensitivity analyses in wind resource assessment / Analyses d'incertitude et de sensibilité dans l'évaluation des ressources éoliennes. - Tsvetkova, Olga (Doctorat en sciences de la terre, 2022)
Cette thèse traite des aspects de la quantification de l'incertitude dans l'évaluation des ressources éoliennes avec les pratiques d'analyses d'incertitude et de sensibilité. Les objectifs de cette thèse sont d'examiner et d'évaluer la qualité des pratiques d'analyse de sensibilité dans l'évaluation des ressources éoliennes, de décourager l'utilisation d'une analyse de sensibilité à la fois, d'encourager l'utilisation d'une analyse de sensibilité globale à la place, d'introduire des méthodes d'autres domaines., et montrer comment les analyses d'incertitude et de sensibilité globale ajoutent de la valeur au processus d'aide à la décision. Cette thèse est organisée en quatre articles : I. Une revue des pratiques d'analyse de sensibilité dans l'évaluation des ressources éoliennes avec une étude de cas de comparaison d'analyses de sensibilité individuelles et globales du coût actualisé de l'énergie éolienne offshore ; II. Technique Quasi-Monte Carlo dans l'analyse de sensibilité globale dans l'évaluation des ressources éoliennes avec une étude de cas sur les Émirats Arabes Unis; III. Utilisation de la famille de distribution Halphen pour l'estimation de la vitesse moyenne du vent avec une étude de cas sur l'Est du Canada; IV. Étude d'évaluation des ressources éoliennes offshore du golfe Persique avec les données satellitaires QuikSCAT.Les articles I à III ont chacun donné lieu à une publication évaluée par des pairs, tandis que l'article IV - à une soumission. L'article I propose des classifications par variable de sortie d'analyse de sensibilité, méthode, application, pays et logiciel. L'article I met en évidence les lacunes de la littérature, fournit des preuves des pièges, conduisant à des résultats d'évaluation erronés et coûteux des ressources éoliennes. L'article II montre comment l'analyse de sensibilité globale offre une amélioration au moyen du quasi-Monte Carlo avec ses plans d'échantillonnage élaborés permettant une convergence plus rapide. L'article III introduit la famille de distribution Halphen pour l'évaluation des ressources éoliennes. Article IV utilise les données satellitaires SeaWinds/QuikSCAT pour l'évaluation des ressources éoliennes offshore du golfe Persique. Les principales contributions à l'état de l'art avec cette thèse suivent. À la connaissance de l'auteur, aucune revue de l'analyse de sensibilité dans l'évaluation des ressources éoliennes n'est actuellement disponible dans la littérature, l'article I en propose une. L'article II relie la modélisation mathématique et l'évaluation des ressources éoliennes en introduisant la technique de quasi-Monte Carlo dans l'évaluation des ressources éoliennes. L'article III présente la famille de distribution de Halphen, de l'analyse de la fréquence des crues à l'évaluation des ressources éoliennes. This dissertation deals with the aspects of quantifying uncertainty in wind resource assessment with the practices of uncertainty and sensitivity analyses. The objectives of this dissertation are to review and assess the quality of sensitivity analysis practices in wind resource assessment, to discourage the use of one-at-a-time sensitivity analysis, encourage the use of global sensitivity analysis instead, introduce methods from other fields, and showcase how uncertainty and global sensitivity analyses adds value to the decision support process. This dissertation is organized in four articles: I. Review article of 102 feasibility studies: a review of sensitivity analysis practices in wind resource assessment with a case study of comparison of one-at-a-time vs. global sensitivity analyses of the levelized cost of offshore wind energy; II. Research article: Quasi-Monte Carlo technique in global sensitivity analysis in wind resource assessment with a case study on United Arab Emirates; III. Research article: Use of the Halphen distribution family for mean wind speed estimation with a case study on Eastern Canada; IV. Application article: Offshore wind resource assessment study of the Persian Gulf with QuikSCAT satellite data. Articles I-III have each resulted in a peer-reviewed publication, while Article IV – in a submission. Article I offers classifications by sensitivity analysis output variable, method, application, country, and software. It reveals the lack of collective agreement on the definition of sensitivity analysis in the literature, the dominance of nonlinear models, the prevalence of one-at a-time sensitivity analysis method, while one-at-a-time method is only valid for linear models. Article I highlights gaps in the literature, provides evidence of the pitfalls, leading to costly erroneous wind resource assessment results. Article II shows how global sensitivity analysis offers improvement by means of the quasi-Monte Carlo with its elaborate sampling designs enabling faster convergence. Article III introduces the Halphen distribution family for the purpose of wind recourse assessment. Article IV uses SeaWinds/QuikSCAT satellite data for offshore wind resource assessment of the Persian Gulf. The main contributions to the state-of-the-art with this dissertation follow. To the best of author’s knowledge, no review of sensitivity analysis in wind resource assessment is currently available in the literature, Article I offers such. Article II bridges mathematical modelling and wind resource assessment by introducing quasi-Monte Carlo technique to wind resource assessment. Article III introduces the Halphen distribution family from flood frequency analysis to wind resource assessment.

Drone-based ground-penetrating radar (GPR) application to snow hydrology - Valence, Eole; Baraer, Michel; Rosa, Eric; Barbecot, Florent; Monty, Chloe (The Cryosphere, 2022)
Seasonal snowpack deeply influences the distribution of meltwater among watercourses and groundwater. During rain-on-snow (ROS) events, the structure and properties of the different snow and ice layers dictate the quantity and timing of water flowing out of the snowpack, increasing the risk of flooding and ice jams. With ongoing climate change, a better understanding of the processes and internal properties influencing snowpack outflows is needed to predict the hydrological consequences of winter melting episodes and increases in the frequency of ROS events. This study develops a multi-method approach to monitor the key snowpack properties in a non-mountainous environment in a repeated and non-destructive way. Snowpack evolution during the winter of 2020–2021 was evaluated using a drone-based, ground-penetrating radar (GPR) coupled with photogrammetry surveys conducted at the Ste-Marthe experimental watershed in Quebec, Canada. Drone-based surveys were performed over a 200 m2 area with a flat and a sloped section. In addition, time domain reflectometry (TDR) measurements were used to follow water flow through the snowpack and identify drivers of the changes in snowpack conditions, as observed in the drone-based surveys. The experimental watershed is equipped with state-of-the-art automatic weather stations that, together with weekly snow pit measurements over the ablation period, served as a reference for the multi-method monitoring approach. Drone surveys conducted on a weekly basis were used to generate georeferenced snow depth, density, snow water equivalent and bulk liquid water content maps. Despite some limitations, the results show that the combination of drone-based GPR, photogrammetric surveys and TDR is very promising for assessing the spatiotemporal evolution of the key hydrological characteristics of the snowpack. For instance, the tested method allowed for measuring marked differences in snow pack behaviour between the first and second weeks of the ablation period. A ROS event that occurred during the first week did not generate significant changes in snow pack density, liquid water content and water equivalent, while another one that happened in the second week of ablation generated changes in all three variables. After the second week of ablation, differences in density, liquid water content (LWC) and snow water equivalent (SWE) between the flat and the sloped sections of the study area were detected by the drone-based GPR measurements. Comparison between different events was made possible by the contact-free nature of the drone-based measurements.
Keywords: Baraer_Michel; Infolettre; Infolettre_Octobre_2022; Site_internet_RIISQ

Spatial–temporal variability of seasonal daily minimum flows in southern Quebec: synthesis on the impacts of climate, agriculture and wetlands - Assani, Ali; Zeroual, Ayoub; Kinnard, Christophe; Roy, Alexandre (Hydrology Research, 2022)
Abstract This study compares the impacts of climate, agriculture and wetlands on the spatio-temporal variability of seasonal daily minimum flows during the period 1930–2019 in 17 watersheds of southern Quebec (Canada). In terms of spatial variability, correlation analysis revealed that seasonal daily minimum flows were mainly negatively correlated with the agricultural surface area in watersheds in spring, summer and fall. In winter, these flows were positively correlated with the wetland surface area and March temperatures but negatively correlated with snowfall. During all four seasons, spatial variability was characterized by higher daily minimum flow values on the north shore (smaller agricultural surface area and larger wetland surface area) than those on the south shore. As for temporal variability, the application of six tests of the long-term trend analysis showed that most agricultural watersheds are characterized by a significant increase in flows during the four seasons due to the reduction in agricultural area, thus favoring water infiltration, and increased rainfall in summer and fall. On the other hand, the reduction in the snowfall resulted in a reduction in summer daily minimum flows observed in several less agricultural watersheds.
Keywords: Kinnard_Christophe

Hydrological behaviour of an ice‐layered snowpack in a non‐mountainous environment - Paquotte, Andrea; Baraer, Michel (Hydrological Processes, 2022)
Abstract The increased frequency of mild rain‐on‐snow (R.O.S.) events in cold regions associated with climate change is projected to affect snowpack structure and hydrological behaviour. The ice layers that form in a cold snowpack when R.O.S. events occur have been shown to influence flowthrough processes and liquid water retention, with consequences for winter floods, groundwater recharge, and water resources management. This study explores interconnections between meteorological conditions, ice layer formation, and lateral flows during R.O.S. events throughout the 2018–2019 winter in meridional Quebec, Canada. Automated hydro‐meteorological measurements, such as water availability for runoff, snow water equivalent, and snowpit observations, are used to compute water and energy balances, making it possible to characterize a snowpack's internal conditions and flowthrough regimes. For compatibility assessment, water and energy balances‐based flowthrough scenarios are then compared to different hydro‐meteorological variables', such as water table or streamlet water levels. The results show an association between highly variable meteorological conditions, frequent R.O.S. events, and ice layer formation. Lateral flows were mainly observed during the early stage of the ablation period. The hydrologically significant lateral flows observed in the study are associated with winter conditions that are predicted to become more frequent in a changing climate, stressing the need for further evaluation of their potential impact at the watershed scale.
Keywords: Baraer_Michel; Infolettre; Infolettre_Ete_2023

Accessing the Impact of Meteorological Variables on Machine Learning Flood Susceptibility Mapping - McGrath, Heather; Gohl, Piper Nora (Remote Sensing, 2022)
Machine learning (ML) algorithms have emerged as competent tools for identifying areas that are susceptible to flooding. The primary variables considered in most of these works include terrain models, lithology, river networks and land use. While several recent studies include average annual rainfall and/or temperature, other meteorological information such as snow accumulation and short-term intense rain events that may influence the hydrology of the area under investigation have not been considered. Notably, in Canada, most inland flooding occurs during the freshet, due to the melting of an accumulated snowpack coupled with heavy rainfall. Therefore, in this study the impact of several climate variables along with various hydro-geomorphological (HG) variables were tested to determine the impact of their inclusion. Three tests were run: only HG variables, the addition of annual average temperature and precipitation (HG-PT), and the inclusion of six other meteorological datasets (HG-8M) on five study areas across Canada. In HG-PT, both precipitation and temperature were selected as important in every study area, while in HG-8M a minimum of three meteorological datasets were considered important in each study area. Notably, as the meteorological variables were added, many of the initial HG variables were dropped from the selection set. The accuracy, F1, true skill and Area Under the Curve (AUC) were marginally improved when the meteorological data was added to the a parallel random forest algorithm (parRF). When the model is applied to new data, the estimated accuracy of the prediction is higher in HG-8M, indicating that inclusion of relevant, local meteorological datasets improves the result.

Multi-Scale Flood Mapping under Climate Change Scenarios in Hexagonal Discrete Global Grids - Li, Mingke; McGrath, Heather; Stefanakis, Emmanuel (ISPRS International Journal of Geo-Information, 2022)
Among the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response and prevention. Machine learning is widely used in flood modeling due to its high performance and scalability. Nonetheless, data pre-processing of heterogeneous sources can be cumbersome, and traditional data processing and modeling have been limited to a single resolution. This study employed an Icosahedral Snyder Equal Area Aperture 3 Hexagonal Discrete Global Grid System (ISEA3H DGGS) as a scalable, standard spatial framework for computation, integration, and analysis of multi-source geospatial data. We managed to incorporate external machine learning algorithms with a DGGS-based data framework, and project future flood risks under multiple climate change scenarios for southern New Brunswick, Canada. A total of 32 explanatory factors including topographical, hydrological, geomorphic, meteorological, and anthropogenic were investigated. Results showed that low elevation and proximity to permanent waterbodies were primary factors of flooding events, and rising spring temperatures can increase flood risk. Flooding extent was predicted to occupy 135–203% of the 2019 flood area, one of the most recent major flooding events, by the year 2100. Our results assisted in understanding the potential impact of climate change on flood risk, and indicated the feasibility of DGGS as the standard data fabric for heterogeneous data integration and incorporated in multi-scale data mining.

Gridded flood depth estimates from satellite-derived inundations - Bryant, Seth; McGrath, Heather; Boudreault, Mathieu (Natural Hazards and Earth System Sciences, 2022)
Abstract. Canada's RADARSAT missions improve the potential to study past flood events; however, existing tools to derive flood depths from this remote-sensing data do not correct for errors, leading to poor estimates. To provide more accurate gridded depth estimates of historical flooding, a new tool is proposed that integrates Height Above Nearest Drainage and Cost Allocation algorithms. This tool is tested against two trusted, hydraulically derived, gridded depths of recent floods in Canada. This validation shows the proposed tool outperforms existing tools and can provide more accurate estimates from minimal data without the need for complex physics-based models or expert judgement. With improvements in remote-sensing data, the tool proposed here can provide flood researchers and emergency managers accurate depths in near-real time.
Keywords: Infolettre; Infolettre_Juillet_2022; Site_internet_RIISQ

Japanese knotweed increases soil erosion on riverbanks - Matte, Rébecca; Boivin, Maxime; Lavoie, Claude (River Research and Applications, 2022)
Abstract For years, Japanese knotweed ( Reynoutria japonica ) has been suspected of accelerating riverbank erosion, despite a lack of convincing evidence. The stems of this invasive plant die back following the first autumn frosts, leaving the soil unprotected during winter and spring floods. In Québec (Canada), riverbank erosion may also be accentuated by ice during mechanical ice breakups. The objective of this study was to evaluate the influence of knotweed on riverbank erosion along a river invaded by the species, within a context of floods with ice. The elevation along 120 cross‐sectional riverbank profiles, occupied or not by knotweed, was measured before and after the spring flood of 2019. On average, riverbanks occupied by knotweed had nearly 3 cm more soil erosion than riverbanks without knotweed, a statistically significant difference. Stem density also influenced erosion: the higher the density, the greater the soil loss. Certain riverside conditions, such as the slope of the riverbank or being located on an islet, interacted with knotweed, further accentuating erosion. Soil losses measured between November 2018 and May–June 2019 were particularly pronounced, but the spring flood was also exceptional, with a recurrence interval close to 50 years. On the other hand, soil loss from rivers invaded by knotweed can be expected to increase over time, as this invasive species spreads rapidly in riparian habitats.
Keywords: Boivin_Maxime; Infolettre; Infolettre_Janvier_2022; Site_internet_RIISQ

Managing residual flood risk behind levees: Comparing USA , France, and Quebec (Canada) - Serra‐Llobet, Anna; Tourment, Rémy; Montané, Antonin; Buffin‐Belanger, Thomas (Journal of Flood Risk Management, 2022)
Abstract Although hydraulic infrastructure such as levees remain important for flood risk management in the USA, France, and Quebec (Canada), there is increasing emphasis on nonstructural measures, such as regulatory flood maps, to reduce exposure and vulnerability, for example, preventing people from building in high hazard areas. One key concept related to areas protected by levees is that of “residual risk”, that is, the risk from floods greater than the design standard of the levees (levee overtopping) and from levee breach. In this article, we review the legislative framework for regulatory flood maps in the USA, France, and Quebec (Canada) and compare how residual risk behind protective structures is taken into account (or not) in regulatory flood maps. We find big differences in how the USA, France and Canada manage residual risk behind the levees. While in France the area behind levees is part of the regulatory flood prone area, and land use restrictions, building codes, emergency measures and risk communication are mandatory, in the USA the area behind levees is only shown as part of the regulatory flood prone area if the levee is not accredited. In Quebec, regulatory flood maps in general follow the French approach with a few exceptions.
Keywords: Infolettre; Infolettre_Avril_2022

A 247‐year tree‐ring reconstruction of spring temperature and relation to spring flooding in eastern boreal Canada - Nolin, Alexandre Florent; Girardin, Martin Philippe; Tardif, Jacques Clément; Guo, Xiao Jing; Conciatori, France; Bergeron, Yves (International Journal of Climatology, 2022)
Abstract Few records of spring paleoclimate are available for boreal Canada, as biological proxies recording the beginning of the warm season are uncommon. Given the spring warming observed during the last decades, and its impact on snowmelt and hydrological processes, searching for spring climate proxies is receiving increasing attention. Tree‐ring anatomical features and intra‐annual widths were used to reconstruct the regional March to May mean air temperature from 1770 to 2016 in eastern boreal Canada. Nested principal component regressions calibrated on 116 years of gridded temperature data were developed from one Fraxinus nigra and 10 Pinus banksiana sites. The reconstruction indicated three distinct phases in spring temperature variability since 1770. Ample phases of multi‐decadal warm and cold springs persisted until the end of the Little Ice Age (1850–1870 CE) and were gradually replaced since the 1940s by decadal to interannual variability associated with an increase in the frequency and magnitude of warm springs. Significant correlations with other paleotemperature records, gridded snow cover extent and runoff support that historical high flooding were associated with late, cold springs with heavy snow cover. Most of the high magnitude spring floods reconstructed for the nearby Harricana River also coincided with the lowest reconstructed spring temperature per decade. However, the last 40 years of observed and reconstructed mean spring temperature showed a reduction in the number of extreme cold springs contrasting with the last few decades of extreme flooding in the eastern Canadian boreal region. This result indicates that warmer late spring mean temperatures on average may contribute, among other factors, to advance the spring break‐up and to likely shift the contribution of snow to rain in spring flooding processes.
Keywords: Infolettre; Infolettre_Avril_2022

Detection of prevalent SARS-CoV-2 variant lineages in wastewater and clinical sequences from cities in Québec, Canada - N’Guessan, Arnaud; Tsitouras, Alexandra; Sanchez-Quete, Fernando; Goitom, Eyerusalem; Reiling, Sarah J.; Galvez, Jose Hector; Nguyen, Thanh Luan; Loan Nguyen, Ha Thanh; Visentin, Flavia; Hachad, Mounia; Krylova, Kateryna; Matthews, Sara; et al. (2022)
ABSTRACT Wastewater-based epidemiology has emerged as a promising tool to monitor pathogens in a population, particularly when clinical diagnostic capacities become overwhelmed. During the ongoing COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), several jurisdictions have tracked viral concentrations in wastewater to inform public health authorities. While some studies have also sequenced SARS-CoV-2 genomes from wastewater, there have been relatively few direct comparisons between viral genetic diversity in wastewater and matched clinical samples from the same region and time period. Here we report sequencing and inference of SARS-CoV-2 mutations and variant lineages (including variants of concern) in 936 wastewater samples and thousands of matched clinical sequences collected between March 2020 and July 2021 in the cities of Montreal, Quebec City, and Laval, representing almost half the population of the Canadian province of Quebec. We benchmarked our sequencing and variant-calling methods on known viral genome sequences to establish thresholds for inferring variants in wastewater with confidence. We found that variant frequency estimates in wastewater and clinical samples are correlated over time in each city, with similar dates of first detection. Across all variant lineages, wastewater detection is more concordant with targeted outbreak sequencing than with semi-random clinical swab sampling. Most variants were first observed in clinical and outbreak data due to higher sequencing rate. However, wastewater sequencing is highly efficient, detecting more variants for a given sampling effort. This shows the potential for wastewater sequencing to provide useful public health data, especially at places or times when sufficient clinical sampling is infrequent or infeasible.
Keywords: Dorner_Sarah

Flood frequency analysis at ungauged catchments with the GAM and MARS approaches in the Montreal region, Canada - Msilini, Amina; Charron, Christian; Ouarda, Taha B. M. J.; Masselot, Pierre (Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2022)
Keywords: Infolettre; Infolettre_Avril_2022; Site_internet_RIISQ

Modelling glacier mass balance and climate sensitivity in the context of sparse observations: application to Saskatchewan Glacier, western Canada - Kinnard, Christophe; Larouche, Olivier; Demuth, Michael N.; Menounos, Brian (The Cryosphere, 2022)
Abstract. Glacier mass balance models are needed at sites with scarce long-term observations to reconstruct past glacier mass balance and assess its sensitivity to future climate change. In this study, North American Regional Reanalysis (NARR) data were used to force a physically based, distributed glacier mass balance model of Saskatchewan Glacier for the historical period 1979–2016 and assess its sensitivity to climate change. A 2-year record (2014–2016) from an on-glacier automatic weather station (AWS) and historical precipitation records from nearby permanent weather stations were used to downscale air temperature, relative humidity, wind speed, incoming solar radiation and precipitation from the NARR to the station sites. The model was run with fixed (1979, 2010) and time-varying (dynamic) geometry using a multitemporal digital elevation model dataset. The model showed a good performance against recent (2012–2016) direct glaciological mass balance observations as well as with cumulative geodetic mass balance estimates. The simulated mass balance was not very sensitive to the NARR spatial interpolation method, as long as station data were used for bias correction. The simulated mass balance was however sensitive to the biases in NARR precipitation and air temperature, as well as to the prescribed precipitation lapse rate and ice aerodynamic roughness lengths, showing the importance of constraining these two parameters with ancillary data. The glacier-wide simulated energy balance regime showed a large contribution (57 %) of turbulent (sensible and latent) heat fluxes to melting in summer, higher than typical mid-latitude glaciers in continental climates, which reflects the local humid “icefield weather” of the Columbia Icefield. The static mass balance sensitivity to climate was assessed for prescribed changes in regional mean air temperature between 0 and 7 ∘C and precipitation between −20 % and +20 %, which comprise the spread of ensemble Representative Concentration Pathway (RCP) climate scenarios for the mid (2041–2070) and late (2071–2100) 21st century. The climate sensitivity experiments showed that future changes in precipitation would have a small impact on glacier mass balance, while the temperature sensitivity increases with warming, from −0.65 to −0.93 m w.e. a−1 ∘C−1. The mass balance response to warming was driven by a positive albedo feedback (44 %), followed by direct atmospheric warming impacts (24 %), a positive air humidity feedback (22 %) and a positive precipitation phase feedback (10 %). Our study underlines the key role of albedo and air humidity in modulating the response of winter-accumulation type mountain glaciers and upland icefield-outlet glacier settings to climate.
Keywords: Kinnard_Christophe

Wastewater Surveillance for SARS-CoV-2 RNA in Canada - Hrudey, Steve E.; Bischel, Heather N.; Charrois, Jeff; Chik, Alex H. S.; Conant, Bernadette; Delatolla, Rob; Dorner, Sarah; Graber, Tyson E.; Hubert, Casey; Isaac-Renton, Judy; Pons, Wendy; Safford, Hannah; et al. (FACETS, 2022)
Wastewater surveillance for SARS-CoV-2 RNA is a relatively recent adaptation of long-standing wastewater surveillance for infectious and other harmful agents. Individuals infected with COVID-19 were found to shed SARS-CoV-2 in their faeces. Researchers around the world confirmed that SARS-CoV-2 RNA fragments could be detected and quantified in community wastewater. Canadian academic researchers, largely as volunteer initiatives, reported proof-of-concept by April 2020. National collaboration was initially facilitated by the Canadian Water Network. Many public health officials were initially skeptical about actionable information being provided by wastewater surveillance even though experience has shown that public health surveillance for a pandemic has no single, perfect approach. Rather, different approaches provide different insights, each with its own strengths and limitations. Public health science must triangulate among different forms of evidence to maximize understanding of what is happening or may be expected. Well-conceived, resourced, and implemented wastewater-based platforms can provide a cost-effective approach to support other conventional lines of evidence. Sustaining wastewater monitoring platforms for future surveillance of other disease targets and health states is a challenge. Canada can benefit from taking lessons learned from the COVID-19 pandemic to develop forward-looking interpretive frameworks and capacity to implement, adapt, and expand such public health surveillance capabilities.
Keywords: Dorner_Sarah

Harmonizing and Extending Fragmented 100 Year Flood Hazard Maps in Canada’s Capital Region Using Random Forest Classification - Bhuiyan, Shelina A.; Bataille, Clement P.; McGrath, Heather (Water, 2022)
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.

Landscape and climate conditions influence the hydrological sensitivity to climate change in eastern Canada - Aygün, Okan; Kinnard, Christophe; Campeau, Stéphane; Pomeroy, John W. (Journal of Hydrology, 2022)
Keywords: Kinnard_Christophe

Impact of Wetland Surface Area on Seasonal Daily Extreme Flow Characteristics during the Summer-Fall Season in Southern Quebec (Canada) - Assani, Ali Arkamose (International Journal of Environmental Sciences & Natural Resources, 2022)
Keywords: Site_internet_RIISQ

Comparison of annual flood characteristics in four watersheds in relation to the wetland surface areas (Southern Quebec, Canada) - Assani, Ali A. (Wetlands Ecology and Management, 2022)

An Integrated Extreme Rainfall Modeling Tool (SDExtreme) for Climate Change Impacts and Adaptation - Yeo, Myeong-Ho; Nguyen, Van-Thanh-Van; Kim, Yong Sang; Kpodonu, Theodore A. (Water Resources Management, 2022)
Abstract The estimation of the Intensity–Duration–Frequency (IDF) relation is often necessary for the planning and design of various hydraulic structures and design storms. It has been an increasingly greater challenge due to climate change conditions. This paper therefore proposes an integrated extreme rainfall modeling software package (SDExtreme) for constructing the IDF relations at a local site in the context of climate change. The proposed tool is based on a temporal downscaling method to describe the relationships between daily and sub-daily extreme precipitation using the scale-invariance General Extreme Value (GEV) distribution. In addition, SDExtreme provides a modified bootstrap technique to determine confidence intervals (CIs) of the estimated IDF curves for current and the future climate conditions. The feasibility and accuracy of SDExtreme were assessed using rainfall data available from the selected rain gauge stations in Quebec and Ontario provinces (Canada) and climate simulations under three different climate change scenarios provided by the Canadian Earth System Model (CanESM2) and the Canadian Regional Climate Model (CanRCM4).
Keywords: Infolettre; Infolettre_Juillet_2022; Site_internet_RIISQ

High-resolution spatiotemporal analysis of hydrologic connectivity in the historical floodplain of straightened lowland agricultural streams - Marchand, Jean-Philippe; Biron, Pascale; Buffin-Bélanger, Thomas; Larocque, Marie (River Research and Applications, 2022)
In agricultural watersheds, human interventions such as channel straightening have disrupted the hydrologic connectivity between headwater streams and their riparian environment and have thus undermined the ecological services provided by these small streams. Knowledge of the hydrologic connectivity between these streams and their immediate environment (shallow riparian groundwater in the historical floodplain and on adjacent hillslopes) in human-impacted settings is critical for understanding and restoring these hydrological systems but remains largely incomplete. The objective of this research is to investigate the hydrogeomorphological conditions controlling hydrologic connectivity in the historical floodplain of straightened lowland streams. Detailed measurements on the spatiotemporal variability of groundwater-surface water interactions between straightened reaches, historical floodplain including abandoned meanders, and the adjacent hillslopes were obtained using a dense network of piezometers at two sites in the St. Lawrence Lowlands (Quebec, Canada). Results show that the complex mechanisms controlling hydrologic connectivity in naturally meandering lowland rivers also operate in highly disturbed straightened reaches, despite backfilling and agricultural practices. The pre-straightening hydrogeomorphological configuration of the floodplain partly explains the complex patterns of piezometric fluctuations observed at the sites. The apex of the abandoned meanders stands out as a focal area of hydrologic connectivity as water levels indicate pressure transfer that may reflect flows from the stream, the hillslopes, and the surrounding historical floodplain. These unique field observations suggest that abandoned meanders should be promoted as key elements of restoration strategies in lowland agricultural straightened headwater streams.
Keywords: Infolettre; Infolettre_Juillet_2022; Site_internet_RIISQ