The Impact of Flood Adaptation Measures on Affected Population’s Mental Health: A mixed method Scoping Review - El-Mousawi, Fatima; Ortiz, Ariel Mundo; Berkat, Rawda; Nasri, Bouchra (Cold Spring Harbor Laboratory, 2023)
AbstractThe frequency and severity of floods has increased in different regions of the world due to climate change. Although the impact of floods on human health has been extensively studied, the increase in the segments of the population that are likely to be impacted by floods in the future makes it necessary to examine how adaptation measures impact the mental health of individuals affected by these natural disasters. The goal of this scoping review is to document the existing studies on flood adaptation measures and their impact on the mental health of affected populations, in order to identify the best preventive strategies as well as limitations that deserve further exploration. This study employed the methodology of the PRISMA-ScR extension for scoping reviews to systematically search the databases Medline and Web of Science to identify studies that examined the impact of adaptation measures on the mental health of flood victims. The database queries resulted in a total of 857 records from both databases. Following two rounds of screening, 9 studies were included for full-text analysis. Most of the analyzed studies sought to identify the factors that drive resilience in flood victims, particularly in the context of social capital (6 studies), whereas the remaining studies analyzed the impact of external interventions on the mental health of flood victims, either from preventive or post-disaster measures (3 studies). There is a very limited number of studies that analyze the impact of adaptation measures on the mental health of populations and individuals affected by floods, which complicates the generalizability of their findings. There is a need for public health policies and guidelines for the development of flood adaptation measures that adequately consider a social component that can be used to support the mental health of flood victims.

Evaluating real-time control of stormwater drainage network and green stormwater infrastructure for enhancing flooding resilience under future rainfall projections - Li, Jiada; Burian, Steven J. (Resources, Conservation and Recycling, 2023)
Traditional stormwater control measures are designed to handle system loadings induced by fixed-size storm events. However, climate change is predicted to alter the frequency and intensity of flooding events, stimulating the need to explore another more adaptive flooding solution like real-time control (RTC). This study assesses the performance of RTC to mitigate impacts of climate change on urban flooding resilience. A simulated, yet realistic, urban drainage system in Salt Lake City, Utah, USA, shows that RTC improves the flooding resilience by up to 17% under climatic rainfall changes. Compared with green stormwater infrastructure (GSI), RTC exhibits a lower resistibility, lower flooding failure level, and higher recovery rate in system performance curves. Results articulate that keeping RTC's performance consistent under ‘back-to-back’ storms requires a tradeoff between upstream dynamical operation and downstream flooding functionality loss. This research suggests that RTC provides a path towards smart and resilient stormwater management strategy.

Impacts et coûts indirects des stresseurs secondaires sur la santé biopsychosociale des sinistrés des inondations de 2019 - Maltais, Danielle; Bourdeau-Brien, Michaël; Gilbert, Simon; Normandin, Julie-Maude; Tchassem Pinlap, Jonas; Généreux, Mélissa; Landaverde, Elsa; Boudreault, Mathieu (2023)
Les inondations de 2017 et 2019 au Québec ont affecté respectivement 293 et 240 municipalités. Ces inondations ont généré une cascade d’évènements stressants (stresseurs primaires et secondaires) qui ont eu des effets sur la santé mentale de la population et retardé le processus de rétablissement des individus. Cette période de rétablissement peut s’échelonner sur plusieurs mois voire plusieurs années. Cette étude s’inscrit dans la spécificité de la recherche mixte mise de l’avant à travers trois stratégies de recherche, réalisées de façon séquentielle : 1) sondage populationnelle réalisé auprès de 680 personnes, 2) analyse de documents produits par les organisations participant au processus de rétablissement social des sinistrés, ou sur des analyses externes portant sur ces interventions de rétablissement et 3) entrevues semi-dirigées auprès de 15 propriétaires occupants ayant complété une demande d’indemnisation à la suite des inondations de 2019 et auprès de 11 professionnels et gestionnaires participant au processus de rétablissement social. Les entrevues semi-dirigées et les questionnaires complétés par les personnes sinistrées lors des inondations de 2019 démontrent que les principales sources de stress ayant des impacts sur la santé et le bien-être des répondants sont : 1) l’absence d’avertissement et la vitesse de la montée des eaux; 2) l’obligation de se relocaliser et la peur d’être victime de pillage; 3) le manque de solidarité et d’empathie de la part de certains employés du MSP; 4) la gestion des conflits familiaux; 5) la gestion de problèmes de santé nouveaux ou préexistants; 6) la complexité des demandes d’indemnisation; 7) la lourdeur et les délais des travaux de nettoyage ou de restauration; 8) les indemnités inférieures aux coûts engendrés par l’inondation; 9) les pertes matérielles subies, particulièrement ceux d’une valeur de plus de 50 000 $; et 10) la diminution anticipée de la valeur de sa résidence. À cela s’ajoute l’insatisfaction à l’égard du programme d’indemnisation du gouvernement du Québec (PGIAF) qui fait plus que doubler la prévalence des symptômes de stress post-traumatique. Les inondations entraînent également une perte de satisfaction ou de bien-être statistiquement significative. La valeur monétaire de cette perte de jouissance peut être exprimée en équivalent salaires. En moyenne, cette diminution du bien-être équivaut à une baisse de salaire de 60 000$ pour les individus ayant vécu une première inondation et à 100 000$ pour les individus ayant vécu de multiples inondations. Ces résultats suggèrent que les coûts indirects et intangibles représentent une part importante des dommages découlant des inondations. Ce projet de recherche vise également à analyser l’application du PGIAF et son influence sur les stresseurs vécus par les sinistrés dans le contexte de la pandémie de COVID-19. La principale recommandation de cette étude repose sur une analyse de documents, un sondage populationnel et des entrevues semi-dirigées. Ainsi, s’attaquer à la réduction de principaux stresseurs nécessite 1) d’améliorer la gouvernance du risque d’inondation, 2) d’intensifier la communication et le support aux sinistrés, et 3) de revoir les mécanismes d’indemnisation existants.

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

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.

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

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

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