Combining quadrat, rake, and echosounding to estimate submerged aquatic vegetation biomass at the ecosystem scale - Botrel, Morgan; Hudon, Christiane; Biron, Pascale M.; Maranger, Roxane (Limnology and Oceanography: Methods, 2023)
Abstract Measuring freshwater submerged aquatic vegetation (SAV) biomass at large spatial scales is challenging, and no single technique can cost effectively accomplish this while maintaining accuracy. We propose to combine and intercalibrate accurate quadrat‐scuba diver technique, fast rake sampling, and large‐scale echosounding. We found that the overall relationship between quadrat and rake biomass is moderately strong (pseudo R 2 = 0.61) and varies with substrate type and SAV growth form. Rake biomass was also successfully estimated from biovolume (pseudo R 2 = 0.57), a biomass proxy derived from echosounding. In addition, the relationship was affected, in decreasing relevance, by SAV growth form, flow velocity, acoustic data quality, depth, and wind conditions. Sequential application of calibrations yielded predictions in agreement with quadrat observations, but echosounding predictions underestimated biomass in shallow areas (< 1 m) while outperforming point estimation in deep areas (> 3 m). Whole‐system quadrat‐equivalent biomass from echosounding differed by a factor of two from point survey estimates, suggesting echosounding is more accurate at larger scales owing to the increased sample size and better representation of spatial heterogeneity. To decide when an individual or a combination of techniques is profitable, we developed a step‐by‐step guideline. Given the risks of quadrat‐scuba diver technique, we recommend developing a one‐time quadrat–rake calibration, followed by the use of rake and echosounding when sampling at larger spatial and temporal scales. In this case, rake sampling becomes a valid ground truthing method for echosounding, also providing valuable species information and estimates in shallow waters where echosounding is inappropriate.

Flood Risk Management via Risk Communication, Cognitive Appraisal, Collective Efficacy, and Community Action - Lin, Carolyn A. (Sustainability, 2023)
Climate change and more frequent severe storms have caused persistent flooding, storm surges, and erosion in the northeastern coastal region of the United States. These weather-related disasters have continued to generate negative environmental consequences across many communities. This study examined how coastal residents’ exposure to flood risk information and information seeking behavior were related to their threat appraisal, threat-coping efficacy, and participation in community action in the context of building social resilience. A random sample of residents of a coastal community in the Northeastern United States was selected to participate in an online survey (N = 302). Key study results suggested that while offline news exposure was weakly related to flood vulnerability perception, online news exposure and mobile app use were both weakly associated with flood-risk information seeking. As flood vulnerability perception was strongly connected to flood severity perception but weakly linked to lower self-efficacy beliefs, flood severity perception was weakly and moderately associated with response-efficacy beliefs and information seeking, respectively. Furthermore, self-efficacy beliefs, response efficacy beliefs, and flood-risk information seeking were each a weak or moderate predictor of collective efficacy beliefs. Lastly, flood risk information-seeking was a strong predictor and collective efficacy beliefs were a weak predictor of community action for flood-risk management. This study tested a conceptual model that integrated the constructs from risk communication, information seeking, and protection motivation theory. Based on the modeling results reflecting a set of first-time findings, theoretical and practical implications are discussed.

Future flow and water temperature scenarios in an impounded drainage basin: implications for summer flow and temperature management downstream of the dam - Khorsandi, Mostafa; St-Hilaire, André; Arsenault, Richard; Martel, Jean-Luc; Larabi, Samah; Schnorbus, Markus; Zwiers, Francis (Climatic Change, 2023)

A Kriging Method for the Estimation of ALS Point-Cloud Accuracy without Ground Truth - Pashaei, Zahra; Guilbert, Éric; Badard, Thierry (Remote Sensing, 2023)
Airborne LiDAR scanning is a promising approach to providing high-resolution products that are appropriate for different applications, such as flood management. However, the vertical accuracy of airborne LiDAR point clouds is not constant and varies in space. Having a better knowledge of their accuracy will assist decision makers in more accurately estimating the damage caused by flood. Data producers often report the total estimation of errors by means of comparison with a ground truth. However, the reliability of such an approach depends on various factors including the sample size, accessibility to ground truth, distribution, and a large enough diversity of ground truth, which comes at a cost and is somewhat unfeasible in the larger scale. Therefore, the main objective of this article is to propose a method that could provide a local estimation of error without any third-party datasets. In this regard, we take advantage of geostatistical ordinary kriging as an alternative accuracy estimator. The challenge of considering constant variation across the space leads us to propose a non-stationary ordinary kriging model that results in the local estimation of elevation accuracy. The proposed method is compared with global ordinary kriging and a ground truth, and the results indicate that our method provides more reliable error values. These errors are lower in urban and semi-urban areas, especially in farmland and residential areas, but larger in forests, due to the lower density of points and the larger terrain variations.
Keywords: Infolettre

Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water-Related Risk Management - Lee, Taesam; Ouarda, Taha B. M. J. (Earth's Future, 2023)
Hydrological time series often present nonstationarities such as trends, shifts, or oscillations due to anthropogenic effects and hydroclimatological variations, including global climate change. For water managers, it is crucial to recognize and define the nonstationarities in hydrological records. The nonstationarities must be appropriately modeled and stochastically simulated according to the characteristics of observed records to evaluate the adequacy of flood risk mitigation measures and future water resources management strategies. Therefore, in the current study, three approaches were suggested to address stochastically nonstationary behaviors, especially in the long-term variability of hydrological variables: as an overall trend, shifting mean, or as a long-term oscillation. To represent these options for hydrological variables, the autoregressive model with an overall trend, shifting mean level (SML), and empirical mode decomposition with nonstationary oscillation resampling (EMD-NSOR) were employed in the hydrological series of the net basin supply in the Lake Champlain-River Richelieu basin, where the International Joint Committee recently managed and significant flood damage from long consistent high flows occurred. The detailed results indicate that the EMD-NSOR model can be an appropriate option by reproducing long-term dependence statistics and generating manageable scenarios, while the SML model does not properly reproduce the observed long-term dependence, that are critical to simulate sustainable flood events. The trend model produces too many risks for floods in the future but no risk for droughts. The overall results conclude that the nonstationarities in hydrological series should be carefully handled in stochastic simulation models to appropriately manage future water-related risks.
Keywords: Infolettre; Infolettre_Octobre_2023; Infolettre_Septembre_2023

Use of Mixed Methods in the Science of Hydrological Extremes: What Are Their Contributions? - Kabo, Raymond; Bourgault, Marc-André; Barrette, Nathalie; Tanguay, Louis (Hydrology, 2023)
Research in hydrological sciences is constantly evolving to provide adequate answers to address various water-related issues. Methodological approaches inspired by mathematical and physical sciences have shaped hydrological sciences from its inceptions to the present day. Nowadays, as a better understanding of the social consequences of extreme meteorological events and of the population’s ability to adapt to these becomes increasingly necessary, hydrological sciences have begun to integrate knowledge from social sciences. Such knowledge allows for the study of complex social-ecological realities surrounding hydrological phenomena, such as citizens’ perception of water resources, as well as individual and collective behaviors related to water management. Using a mixed methods approach to combine quantitative and qualitative approaches has thus become necessary to understand the complexity of hydrological phenomena and propose adequate solutions for their management. In this paper, we detail how mixed methods can be used to research flood hydrology and low-flow conditions, as well as in the management of these hydrological extremes, through the analysis of case studies. We frame our analysis within the three paradigms (positivism, post-positivism, and constructivism) and four research designs (triangulation, complementary, explanatory, and exploratory) that guide research in hydrology. We show that mixed methods can notably contribute to the densification of data on extreme flood events to help reduce forecasting uncertainties, to the production of knowledge on low-flow hydrological states that are insufficiently documented, and to improving participatory decision making in water management and in handling extreme hydrological events.
Keywords: Infolettre; Infolettre_Ete_2023

Design and Implementation of Machine Learning Models and Algorithms for Flood, Drought and Frazil Prediction - Yagnik, Bhargav Charudatt (Concordia University, 2023)
Natural calamities like floods and droughts pose a significant threat to humanity, impacting millions of people each year and incurring substantial economic losses to society. In response to this challenge, this thesis focuses on developing advanced machine learning techniques to improve water height prediction accuracy that can aid municipalities in effective flood mitigation. The primary objective of this study is to evaluate an innovative architecture that leverages Long Short Term Networks - neural networks to predict water height accurately in three different environmental scenarios, i.e., frazil, droughts and floods due to snow spring melt. A distinguishing feature of our approach is the incorporation of meteorological forecast as an input parameter into the prediction model. By modeling the intricate relationships between water level data, historical meteorological data and meteorological forecasts, we seek to evaluate the impact of meteorological forecasts and if any inaccuracies could impact water-level prediction. We compare the outcomes obtained by incorporating next-hour, next-day and next-week meteorological data into our novel LSTM model. Our results indicate a comprehensive comparison of the usage of various parameters as input and our findings suggest that accurate weather forecasts are crucial in achieving reliable water height predictions. Additionally, this study focuses on the utilization of IoT sensor data in combination with ML models to enhance the effectiveness of flood prediction and management. We present an online machine learning approach that performs online training of the model using real-time data from IoT sensors. The integration of live sensor data provides a dynamic and adaptive system that demonstrates superior predictive capabilities compared to traditional static models. By adopting these advanced techniques, we can mitigate the adverse impacts of natural catastrophes and work towards building more resilient and disaster-resistant communities.

Improving hydrological forecasting at multiple lead-times for hydropower reservoir management - Sabzipour, Behmard (École de technologie supérieure, 2023)
Streamflow forecasting is important for managing water resources in sectors like agriculture, hydropower, drought management, and urban flood prevention planning. Our study examines short and long lead-times to create a framework for streamflow forecasting that can benefit water resource management and related sectors. To improve streamflow forecasts for up to ten days of lead-time, the study first focuses on improving initial conditions using an ensemble Kalman filter as a data assimilation method. The goal is to regulate the hyperparameters of the ensemble Kalman filter for each season to produce more accurate forecasts. A sensitivity analysis is conducted to identify the best hyperparameter sets for each season, including uncertainty in temperature, precipitation, observed streamflow, and the water content of three state variables - vadose zone, saturated zone, and snowpack - from the CEQUEAU model. Results indicate that improving initial conditions with the ensemble Kalman filter produces more skillful forecasts until a 6-day leadtime. Temperature uncertainty is particularly sensitive and varies across seasons. The vadose zone state variable was identified as the most important and sensitive state variable, and updating all state variables systematically may not be necessary for improving forecast skill. Recent machine learning advances are improving short-term streamflow forecasting. One such method is the Long Short-Term Memory (LSTM) model. In general, neural networks learn from regression as relationships exist between input-output. However, LSTM models have a feature named ‘forget gate’, which enables them to learn the relationship between inputs (e.g., temperature and precipitation) and output (streamflow), and also to capture temporal dependencies in the data. The study aimed to compare the performance of the Long ShortTerm Memory (LSTM) model with data assimilation-based and process-based hydrological models in short-term streamflow forecasting. All three models were tested using the same ensemble weather forecasts. The LSTM model demonstrated good performance in forecasting streamflow, with a Kling-Gupta efficiency (KGE) greater than 0.88 for 9 lead-times. The LSTM model did not incorporate data assimilation, but it benefited from observed streamflow until the last day before the forecast. This is because the LSTM model learned and incorporated knowledge from the previous days while issuing forecasts, similar to how data assimilation updates initial conditions. The study results also showed that the LSTM model had better performance up to day 6 of lead-time compared to the data assimilation-based models. However, training the LSTM model separately for each lead-time is a time-consuming process and is a disadvantage compared to the data assimilation-based methods. Nonetheless, the study demonstrated the potential of machine learning techniques in improving streamflow forecasting. The forecasting of streamflow for long lead-times such as a month usually involves the use of historical meteorological data to create probable future scenarios, as meteorological forecasts become unreliable beyond this lead-time. In this study, we proposed a novel method for streamflow forecasting based on ensemble streamflow forecasting (ESP) filtering, using a Genetic Algorithm (GA) to filter forecast scenarios. This method quantifies the potential of historical data for each basin. This potential could be utilized to enhance the accuracy of streamflow forecasts. We sorted the selected and unselected scenarios to find out the common features between them, but the results did not help distinguish between the two groups. Nonetheless, the GA method can be used as a benchmark for future studies to improve longterm streamflow forecasting. This method can also be used to compare different forecast methods based on the potential shown by the GA method for a specific size of ESP members. For instance, if a method uses large-scale climate signals to filter ESP members, the forecast skill result could be compared with the potential of historical data for that particular size of ESP members.

Monitoring of cyanobacterial breakthrough and accumulation by in situ phycocyanin probe system within full-scale treatment plants - Ma, Liya; Maldonado, Juan Francisco Guerra; Zamyadi, Arash; Dorner, Sarah; Prévost, Michèle (Environmental Monitoring and Assessment, 2023)
Abstract Worldwide, there has been an increase in the presence of potentially toxic cyanobacterial blooms in drinking water sources and within drinking water treatment plants (DWTPs). The objective of this study is to validate the use of in situ probes for the detection and management of cyanobacterial breakthrough in high and low-risk DWTPs. In situ phycocyanin YSI EXO2 probes were devised for remote control and data logging to monitor the cyanobacteria in raw, clarified, filtered, and treated water in three full-scale DWTPs. An additional probe was installed inside the sludge holding tank to measure the water quality of the surface of the sludge storage tank in a high-risk DWTP. Simultaneous grab samplings were carried out for taxonomic cell counts and toxin analysis. A total of 23, 9, and 4 field visits were conducted at the three DWTPs. Phycocyanin readings showed a 93-fold fluctuation within 24 h in the raw water of the high cyanobacterial risk plant, with higher phycocyanin levels during the afternoon period. These data provide new information on the limitations of weekly or daily grab sampling. Also, different moving averages for the phycocyanin probe readings can be used to improve the interpretation of phycocyanin signal trends. The in situ probe successfully detected high cyanobacterial biovolumes entering the clarification process in the high-risk plant. Grab sampling results revealed high cyanobacterial biovolumes in the sludge for both high and low-risk plants.
Keywords: Dorner_Sarah

Visual assessment of tree vigour in Canadian northern hardwood forests: The need for a simplified system - Moreau, Guillaume; Cecil-Cockwell, Malcolm J.L.; Pothier, David; Achim, Alexis; Bédard, Steve; Guillemette, François; Caspersen, John (Forest Ecology and Management, 2023)
Keywords: Guillemette_Francois

Quantifying the Probability of Decline in Quality: Implications for Selection Management in Northern Hardwood Forests - Moreau, Guillaume; Cecil-Cockwell, Malcolm J. L.; Achim, Alexis; Bédard, Steve; Guillemette, François; Caspersen, John (Forests, 2023)
Northern hardwoods are susceptible to a wide range of defects that can reduce the amount of sound wood with desirable qualities, such as the clear sapwood of sugar maple trees. Yet, the rate at which trees decline in quality due to the development of such defects has never been quantified in northern hardwood forests due to a dearth of repeat inventories that record the appearance of defects over time. As a result, it remains uncertain whether, and how, selection management reduces the probability of decline in quality. In this study, we quantify the rate at which trees decline in quality due to the development of defects, and we test several hypotheses regarding the influence of selection management on quality. Our results show that (1) the probability of decline in quality increases as trees grow larger; (2) crown dieback also increases the probability of decline in quality; (3) the probability of decline in quality is slightly lower in managed stands than in unmanaged stands, and (4) the probability of decline in quality increases with the mean annual temperature of the site. Finally, we combined our estimates of the probability of decline in quality with previous estimates of the probability of mortality to assess the overall risk associated with retaining trees of different species, sizes, and vigour profiles. The resulting metric can inform efforts to improve the management of northern hardwood forests by providing an integrated estimate of the risk that the value of a tree will be reduced, or eliminated, due to mortality or decline in quality.
Keywords: Guillemette_Francois

Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar - Dharmadasa, Vasana; Kinnard, Christophe; Baraër, Michel (The Cryosphere, 2023)
Abstract. Accurate knowledge of snow depth distributions in forested regions is crucial for applications in hydrology and ecology. In such a context, understanding and assessing the effect of vegetation and topographic conditions on snow depth variability is required. In this study, the spatial distribution of snow depth in two agro-forested sites and one coniferous site in eastern Canada was analyzed for topographic and vegetation effects on snow accumulation. Spatially distributed snow depths were derived by unmanned aerial vehicle light detection and ranging (UAV lidar) surveys conducted in 2019 and 2020. Distinct patterns of snow accumulation and erosion in open areas (fields) versus adjacent forested areas were observed in lidar-derived snow depth maps at all sites. Omnidirectional semi-variogram analysis of snow depths showed the existence of a scale break distance of less than 10 m in the forested area at all three sites, whereas open areas showed comparatively larger scale break distances (i.e., 11–14 m). The effect of vegetation and topographic variables on the spatial variability in snow depths at each site was investigated with random forest models. Results show that the underlying topography and the wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. These results highlight the importance of including and better representing these processes in physically based models for accurate estimates of snowpack dynamics.
Keywords: Baraer_Michel; Kinnard_Christophe

Understanding the shift toward a risk-based approach in flood risk management, a comparative case study of three Italian rivers - Vitale, Corinne (Environmental Science & Policy, 2023)

Public-private flood management: Who should pay and when? - Bouchard St-Amant, Pier-André; Tchokouagueu, Franck Aurelien; Dumais, Guillaume (Heliyon, 2023)

Finding the Voice of the River: Beyond Restoration and ManagementFinding the Voice of the River: Beyond Restoration and ManagementGary J.Brierley. Cham, Switzerland: Palgrave Pivot, 2020. xviii and 179 pp., references, index. $59.99 hardcover (ISBN 978-3-030-27067-4), $44.99 electronic (ISBN 978-3-030-27068-1).: Gary J. Brierley. Cham, Switzerland: Palgrave Pivot, 2020. xviii and 179 pp., references, index. $59.99 hardcover (ISBN 978-3-030-27067-4), $44.99 electronic (ISBN 978-3-030-27068-1). - Tooth, Stephen (The AAG Review of Books, 2023)
Keywords: Ouvrage_Collectif_Numéro1

Development of tree recruitment models for 10 species groups in the sugar maple-dominated mixed forests of eastern Canada - Rijal, Baburam; Power, Hugues; Auger, Isabelle; Guillemette, François; Bédard, Steve; Schneider, Robert (Canadian Journal of Forest Research, 2023)
Individual tree recruitment is an important element needed to understand stand dynamics, as it influences both stand composition and productivity. Forest growth simulators usually include recruitment models. The quality of recruitment predictions can have long-term impacts on estimations of forest growth, ecosystem health and the commercial utility of managed forests. The main objective of this study was to develop a recruitment model for commercial-size trees (i.e., trees with a diameter at breast height > 9 cm) of 10 species groups using different dendrometric and environmental variables. The resulting model will be included in a growth simulator used to support forest management planning. We hypothesized that accounting for sapling density as a covariate would improve the recruitment model's predictive performance. Using empirical data from periodically measured permanent sample plots (1982–2019) located throughout the managed mixed hardwood forests of Quebec, we constructed models with and without sapling-related covariates and compared them on the basis of cross-validation model performance statistics. Our results show that including sapling density significantly improved model performance. From this, we conclude that adding sapling density as a covariate can significantly improve a recruitment model's predictive power for eastern mixed hardwood forests.
Keywords: Guillemette_Francois

Using the theory of planned behavior to identify key beliefs underlying flood-related adaptive behaviors in the province of Québec, Canada - Jacob, Johann; Valois, Pierre; Tessier, Maxime; Talbot, Denis; Anctil, François; Cloutier, Geneviève; Renaud, Jean-Sébastien (Journal of Flood Risk Management, 2023)
Given that flooding episodes are occurring at a greater rate due to climate change, individuals must adopt certain adaptation behaviors to prevent or mitigate the anticipated or negative impact of such events. However, few studies have assessed if and how households and individuals have actually taken action in this regard. Because some individual beliefs can be linked to facilitating factors and barriers to action, a better understanding of the adoption of adaptive behaviors requires a combined analysis of individual psychosocial factors. The purpose of this study was to develop a better understanding of the reasons underlying the adoption of behaviors related to structural adaptation to flooding by people living in or near flood-prone areas in the Province of Québec (Canada). Results of a series of structural equation modeling showed that behavioral, normative and control beliefs were all significant predictors of the respondents' intention to adopt structural flood protective behaviors, with normative beliefs being the strongest. By identifying the best psychosocial predictors of the adoption of such behaviors, the results of this study provide valuable insights regarding the most effective factors to be used in public health messages to promote the adoption of behaviors related to structural adaptation to flooding.
Keywords: Infolettre; Infolettre_Mai_2023

Setting Targets for Wetland Restoration to Mitigate Climate Change Effects on Watershed Hydrology - Goyette, Jean-Olivier; Savary, Stéphane; Blanchette, Marianne; Rousseau, Alain N.; Pellerin, Stéphanie; Poulin, Monique (Environmental Management, 2023)
Keywords: Infolettre; Infolettre_Fevrier_2023; Rousseau_Alain

Toward a framework for systemic multi-hazard and multi-risk assessment and management - Hochrainer-Stigler, Stefan; Šakić Trogrlić, Robert; Reiter, Karina; Ward, Philip J.; De Ruiter, Marleen C.; Duncan, Melanie J.; Torresan, Silvia; Ciurean, Roxana; Mysiak, Jaroslav; Stuparu, Dana; Gottardo, Stefania (iScience, 2023)

Wetland-based solutions against extreme flood and severe drought: Efficiency evaluation of risk mitigation - Wu, Yanfeng; Sun, Jingxuan; Hu, Boting; Zhang, Guangxin; Rousseau, Alain N. (Climate Risk Management, 2023)
Keywords: Infolettre; Infolettre_Juin_2023; Rousseau_Alain

Urban flood resilience, a multi-level institutional analysis of planning practices in the Metropolitan City of Naples - Vitale, Corinne; Meijerink, Sander; Moccia, Francesco Domenico (Journal of Environmental Planning and Management, 2023)
Keywords: Ouvrage_Collectif_Numéro1

Vulnerable Waters are Essential to Watershed Resilience - Lane, Charles R.; Creed, Irena F.; Golden, Heather E.; Leibowitz, Scott G.; Mushet, David M.; Rains, Mark C.; Wu, Qiusheng; D’Amico, Ellen; Alexander, Laurie C.; Ali, Genevieve A.; Basu, Nandita B.; Bennett, Micah G.; et al. (Ecosystems, 2023)
Abstract Watershed resilience is the ability of a watershed to maintain its characteristic system state while concurrently resisting, adapting to, and reorganizing after hydrological (for example, drought, flooding) or biogeochemical (for example, excessive nutrient) disturbances. Vulnerable waters include non-floodplain wetlands and headwater streams, abundant watershed components representing the most distal extent of the freshwater aquatic network. Vulnerable waters are hydrologically dynamic and biogeochemically reactive aquatic systems, storing, processing, and releasing water and entrained (that is, dissolved and particulate) materials along expanding and contracting aquatic networks. The hydrological and biogeochemical functions emerging from these processes affect the magnitude, frequency, timing, duration, storage, and rate of change of material and energy fluxes among watershed components and to downstream waters, thereby maintaining watershed states and imparting watershed resilience. We present here a conceptual framework for understanding how vulnerable waters confer watershed resilience. We demonstrate how individual and cumulative vulnerable-water modifications (for example, reduced extent, altered connectivity) affect watershed-scale hydrological and biogeochemical disturbance response and recovery, which decreases watershed resilience and can trigger transitions across thresholds to alternative watershed states (for example, states conducive to increased flood frequency or nutrient concentrations). We subsequently describe how resilient watersheds require spatial heterogeneity and temporal variability in hydrological and biogeochemical interactions between terrestrial systems and down-gradient waters, which necessitates attention to the conservation and restoration of vulnerable waters and their downstream connectivity gradients. To conclude, we provide actionable principles for resilient watersheds and articulate research needs to further watershed resilience science and vulnerable-water management.

Intersectoral approaches: the key to mitigating psychosocial and health consequences of disasters and systemic risks - Gousse-Lessard, Anne-Sophie; Gachon, Philippe; Lessard, Lily; Vermeulen, Valérie; Boivin, Maxime; Maltais, Danielle; Landaverde, Elsa; Généreux, Mélissa; Motulsky, Bernard; Le Beller, Julien (Disaster Prevention and Management: An International Journal, 2023)
Purpose The current pandemic and ongoing climate risks highlight the limited capacity of various systems, including health and social ones, to respond to population-scale and long-term threats. Practices to reduce the impacts on the health and well-being of populations must evolve from a reactive mode to preventive, proactive and concerted actions beginning at individual and community levels. Experiences and lessons learned from the pandemic will help to better prevent and reduce the psychosocial impacts of floods, or other hydroclimatic risks, in a climate change context. Design/methodology/approach The present paper first describes the complexity and the challenges associated with climate change and systemic risks. It also presents some systemic frameworks of mental health determinants, and provides an overview of the different types of psychosocial impacts of disasters. Through various Quebec case studies and using lessons learned from past and recent flood-related events, recommendations are made on how to better integrate individual and community factors in disaster response. Findings Results highlight the fact that people who have been affected by the events are significantly more likely to have mental health problems than those not exposed to flooding. They further demonstrate the adverse and long-term effects of floods on psychological health, notably stemming from indirect stressors at the community and institutional levels. Different strategies are proposed from individual-centered to systemic approaches, in putting forward the advantages from intersectoral and multirisk researches and interventions. Originality/value The establishment of an intersectoral flood network, namely the InterSectoral Flood Network of Québec (RIISQ), is presented as an interesting avenue to foster interdisciplinary collaboration and a systemic view of flood risks. Intersectoral work is proving to be a major issue in the management of systemic risks, and should concern communities, health and mental health professionals, and the various levels of governance. As climate change is called upon to lead to more and more systemic risks, close collaboration between all the areas concerned with the management of the factors of vulnerability and exposure of populations will be necessary to respond effectively to damages and impacts (direct and indirect) linked to new meteorological and compound hazards. This means as well to better integrate the communication managers into the risk management team.
Keywords: Boivin_Maxime; Gachon_Philippe; Gousse-Lessard_Anne-Sophie; Généreux_Mélissa; Infolettre; Infolettre_Fevrier_2023; Lessard_Lily; Site_internet_RIISQ

B’ the Change you Want to See: How Commensurating Water Health Impedes Collective Action - Couture, Fannie; Le, Jane Kirsten (Academy of Management Proceedings, 2023)

A novel hybrid GIS ‐based multi‐criteria decision‐making approach for flood susceptibility analysis in large ungauged watersheds - Sahraei, Roya; Kanani‐Sadat, Yousef; Homayouni, Saeid; Safari, Abdolreza; Oubennaceur, Khalid; Chokmani, Karem (Journal of Flood Risk Management, 2023)
Abstract Characterizing and identification of flood‐susceptible areas can be a solution to mitigate the damages and fatality rate. This study proposes a novel hybrid MCDM framework to assess flood susceptibility in large ungauged watersheds dealing with data scarcity issues. The proposed method examines the interdependencies and causal relationships between various criteria affecting the flooding procedure using the DEcision‐MAking Trial and Evaluation Laboratory (DEMATEL). Moreover, since experts' opinions contain uncertainty, the fuzzy logic is integrated with DEMATEL to overcome this shortcoming. Then, the local weights of criteria were estimated using the Best–Worst Method (BWM) to enhance the pairwise comparisons process. Final criteria weights were obtained using Fuzzy DEMATEL and BWM results in Analytical Network Process (ANP) super‐matrix. Finally, the criteria were distributed spatially using the Complex Proportional Assessment of Alternatives (COPRAS) method based on obtained weights. The proposed method was compared with different approaches such as Fuzzy‐DEMATEL ANP, BWM, and AHP using several statistical measures. We concluded that the novel hybrid proposed method outperformed other approaches based on our results. Moreover, by overlaying classified maps with the historical flood events locations, it was concluded that 85.96% of flooded areas were classified as “high” and “very high.”
Keywords: Infolettre; Infolettre_Fevrier_2023

Triggers, Traps, and Disconnect: How Governance Obstacles Hinder Progress on Grand Challenges - Couture, Fannie; Jarzabkowski, Paula; Lê, Jane K. (Academy of Management Journal, 2023)

Imported Project Management Practices in Developing Countries: The Problem of Insufficient Adaptation to Local Project Governance Systems in the Construction Sector - Lizarralde, Gonzalo; Páez, Holmes; Herazo, Benjamin (Domain-Specific Bodies of Knowledge in Project Management, 2023)
Keywords: Lizarralde_Gonzalo

Scoping review: understanding the barriers and drivers of risk management in local governments - Bourgeois, Eve; Baril, Pierre-Luc; Normandin, Julie-Maude; Therrien, Marie-Christine (International Journal of Disaster Resilience in the Built Environment, 2023)
Purpose This paper aims to provide scholars with a deep understanding of the field through the identification of strengths and weaknesses in the literature and support decision-makers in the development of new practices in local risk management based on scientific data. The specific question in this review asks: what are the drivers and barriers to local risk management? Design/methodology/approach This paper provides an overview of the scientific literature produce over the past 20 years of the divers and barriers to local risk management. This paper presents a scoping review of peer-reviewed articles published between 2000 and 2019 inclusively in the fields of public policy and public administration. Findings This paper makes three main observations regarding the state of the literature. First, this paper finds that scholars mainly focus on single risk and certain regions of the world. Second, there is multiple approached used by the literature to study risk management at the local level. Third, little attention is given to the political context in which local risk management takes place. Originality/value This paper is a complete literature review of more than 500 peer-reviewed articles published in academic journals regarding risk prevention policies over the past two decades. This paper analyzed the main findings of the current literature to provide a general view of the scholarship and improve the collective understanding of risk management at the local level by providing future research avenues.
Keywords: Infolettre; Infolettre_Mars_2023