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ABSTRACT The increasing frequency of natural disasters, such as floods, droughts, and tsunamis, has made vulnerable communities less resilient, pushing them toward long‐term poverty and food insecurity. Effective post‐disaster rehabilitation is critical to restoring livelihoods, infrastructure, and food security. However, challenges such as corruption, misallocation, and mistargeting undermine post‐disaster aid programs. This study systematically reviews 86 peer‐reviewed articles (1990–2023) using the preferred reporting items for systematic reviews and meta‐analyses (PRISMA) protocol to investigate aid inefficiencies in disaster recovery. The findings reveal that aid often fails to reach the most affected communities, being diverted to unaffected areas due to political influence and local elites, exacerbating inequalities. Corruption further hampers institutional performance and long‐term disaster resilience efforts. The study calls for transparent, accountable, and inclusive strategies for aid distribution, aligning with SDG 10 (reduced inequalities) and SDG 11 (sustainable cities and communities). Future research should focus on gender‐sensitive strategies, local governance, and technological innovations to enhance aid transparency and effectiveness.

Water risk management has been adversely affected by climate variations, including recent climate change. Climate variations have highly impacted the hydrological cycles in the atmosphere and biosphere, and their impact can be defined with the teleconnection between climate signals and hydrological variables. Water managers should practice future risk management to mitigate risks, including the impact of teleconnection, and stochastically simulated scenarios can be employed as an effective tool to take advantage of water management preparation. A stochastic simulation model for hydrological variables teleconnected with climate signals is very useful for water managers. Therefore, the objective of the current study was to develop a novel stochastic simulation model for the simulation of synthetic series teleconnected with climate signals. By jointly decomposing the hydrological variables and a climate signal with bivariate empirical mode decomposition (BEMD), the bivariate nonstationary oscillation resampling (B-NSOR) model was applied to the significant components. The remaining components were simulated with the newly developed method of climate signal-led K-nearest neighbor-based local linear regression (CKLR). This entire approach is referred to as the climate signal-led hydrologic stochastic simulation (CSHS) model. The key statistics were estimated from the 200 simulated series and compared with the observed data, and the results showed that the CSHS model could reproduce the key statistics including extremes while the SML model showed slight underestimation in the skewness and maximum values. Additionally, the observed long-term variability of hydrological variables was reproduced well with the CSHS model by analyzing drought statistics. Moreover, the Hurst coefficient with slightly higher than 0.8 was fairly preserved by the CSHS model while the SML model is underestimated as 0.75. The overall results demonstrate that the proposed CSHS model outperformed the existing shifting mean level (SML) model, which has been used to simulate hydroclimatological variables. Future projections until 2100 were obtained with the CSHS model. The overall results indicated that the proposed CSHS model could represent a reasonable alternative to teleconnect climate signals with hydrological variables.

Floods and droughts cause large economic and environmental impacts and incalculable human suffering. Despite growing evidence of important synergies in their management, floods and droughts tend to be mostly managed in silos. The synergistic management of flood and drought risk is limited by the inability of current governance systems to change at the scope, depth and speed required to address the emerging challenges of climate change induced hydroclimatic risks. Building on the concept of continuous transformational change and combining key elements across sectoral governance frameworks, this paper proposes a transformative governance conceptual framework that enables national governments to work across silos in a whole of government approach to lead a whole of society effort to manage the whole hydroclimatic spectrum. Spain, a country with an advanced hydroclimatic risk management system, is presented as an illustrative example to explore the possible idiosyncrasies of implementing the proposed changes on the ground. © 2025 Núñez Sánchez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

The efficient and rational development of hydropower in the Lancang–Mekong River Basin can promote green energy transition, reduce carbon emissions, prevent and mitigate flood and drought disasters, and ensure the sustainable development of the entire basin. In this study, based on publicly available hydrometeorological observation data and satellite remote sensing monitoring data from 2001 to 2020, a machine learning model of the Lancang–Mekong Basin was developed to reconstruct the basin’s hydrological processes, and identify the occurrence patterns and influencing mechanisms of water-related hazards. The results show that, against the background of climate change, the Lancang–Mekong Basin is affected by the increasing frequency and intensity of extreme precipitation events. In particular, Rx1day, Rx5day, R10mm, and R95p (extreme precipitation indicators determined by the World Meteorological Organization’s Expert Group on Climate Change Monitoring and Extreme Climate Events) in the northwestern part of the Mekong River Basin show upward trends, with the average maximum daily rainfall increasing by 1.8 mm/year and the total extreme precipitation increasing by 18 mm/year on average. The risks of flood and drought disasters will continue to rise. The flood peak period is mainly concentrated in August and September, with the annual maximum flood peak ranging from 5600 to 8500 m3/s. The Stung Treng Station exhibits longer drought duration, greater severity, and higher peak intensity than the Chiang Saen and Pakse Stations. At the Pakse Station, climate change and hydropower development have altered the non-drought proportion by −12.50% and +15.90%, respectively. For the Chiang Saen Station, the fragmentation degree of the drought index time series under the baseline, naturalized, and hydropower development scenarios is 0.901, 1.16, and 0.775, respectively. These results indicate that hydropower development has effectively reduced the frequency of rapid drought–flood transitions within the basin, thereby alleviating pressure on drought management efforts. The regulatory role of the cascade reservoirs in the Lancang River can mitigate risks posed by climate change, weaken adverse effects, reduce flood peak flows, alleviate hydrological droughts in the dry season, and decrease flash drought–flood transitions in the basin. The research findings can enable basin managers to proactively address climate change, develop science-based technical pathways for hydropower dispatch, and formulate adaptive disaster prevention and mitigation strategies. © 2025 by the authors.

Based on the panel data of daily meteorological stations and winter wheat yield in Henan Province from 2000 to 2023, this study comprehensively used the Mann–Kendall trend test, wavelet coherence analysis (WTC), and other methods to reveal the temporal and spatial evolution of extreme precipitation and its multi-scale stress mechanism on grain yield. The results showed the following: (1) Extreme precipitation showed the characteristics of ‘frequent fluctuation-gentle trend-strong spatial heterogeneity’, and the maximum daily precipitation in spring (RX1DAY) showed a significant uplift. The increase in rainstorm events (R95p/R99p) in the southern region during the summer is particularly prominent; at the same time, the number of consecutive drought days (CDDs > 15 d) in the middle of autumn was significantly prolonged. It was also found that 2010 is a significant mutation node. Since then, the synergistic effect of ‘increasing drought days–increasing rainstorm frequency’ has begun to appear, and the short-period coherence of super-strong precipitation (R99p) has risen to more than 0.8. (2) The spatial pattern of winter wheat in Henan is characterized by the three-level differentiation of ‘stable core area, sensitive transition zone and shrinking suburban area’, and the stability of winter wheat has improved but there are still local risks. (3) There is a multi-scale stress mechanism of extreme precipitation on winter wheat yield. The long-period (4–8 years) drought and flood events drive the system risk through a 1–2-year lag effect (short-period (0.5–2 years) medium rainstorm intensity directly impacted the production system). This study proposes a ‘sub-scale governance’ strategy, using a 1–2-year lag window to establish a rainstorm warning mechanism, and optimizing drainage facilities for high-risk areas of floods in the south to improve the climate resilience of the agricultural system against the background of climate change. © 2025 by the authors.

Extreme weather events (EWEs), including floods, droughts, heatwaves and storms, are increasingly recognised as major drivers of biodiversity loss and ecosystem degradation. In this systematic review, we synthesise 251 studies documenting the impacts of extreme weather events on freshwater, terrestrial and marine ecosystems, with the goal of informing effective conservation and management strategies for areas of special conservation or protection focus in Ireland.Twenty-two of the reviewed studies included Irish ecosystems. In freshwater systems, flooding (34 studies) was the most studied EWE, often linked to declines in species richness, abundance and ecosystem function. In terrestrial ecosystems, studies predominantly addressed droughts (60 studies) and extreme temperatures (48 studies), with impacts including increase in mortality, decline in growth and shift in species composition. Marine and coastal studies focused largely on storm events (33 studies), highlighting physical damages linked to wave actions, behavioural changes in macrofauna, changes in species composition and distribution, and loss in habitat cover. Results indicate that most EWEs lead to negative ecological responses, although responses are context specific.While positive responses to EWEs are rare, species with adaptive traits displayed some resilience, especially in ecosystems with high biodiversity or refuge areas.These findings underscore the need for conservation strategies that incorporate EWE projections, particularly for protected habitats and species. © 2025 Royal Irish Academy. All rights reserved.

Les événements météorologiques extrêmes (EME) et les désastres qu’ils entrainent provoquent des conséquences psychosociales qui sont modulées en fonction de différents facteurs sociaux. On constate aussi que les récits médiatiques et culturels qui circulent au sujet des EME ne sont pas représentatifs de l’ensemble des expériences de personnes sinistrées : celles qui en subissent les conséquences les plus sévères tendent aussi à être celles qu’on « entend » le moins dans l’espace public. Ces personnes sont ainsi susceptibles de vivre de l’injustice épistémique, ce qui a des effets délétères sur le soutien qu’elles reçoivent. Face à ces constats s’impose la nécessité de mieux comprendre la diversité des expériences d’EME et d’explorer des stratégies pour soutenir l’ensemble des personnes sinistrées dans leur rétablissement psychosocial. Cet article soutient que la recherche narrative peut contribuer à répondre à ces objectifs. En dépeignant des réalités multiples, la recherche narrative centrée sur les récits de personnes sinistrées présente aussi un intérêt significatif pour l’amélioration des pratiques d’intervention en contexte de désastre. , Extreme weather events (EWE) and their resulting disasters cause psychosocial consequences that are moderated by different social factors. Media and cultural accounts of EWEs do not represent the full range of disaster survivor experiences, that is, those who experienced the most severe consequences also tend to be those least “heard” in the public arena. These people are therefore most likely to experience forms of epistemic injustice that negatively impact the support offered to cope with disaster. Considering these findings, there is a need to better understand the diversity of EWE experiences and explore strategies for supporting all disaster survivors in their psychosocial recovery. This article argues that narrative research can help meet these needs. By portraying the multiple realities of people affected by EWEs, narrative research focusing on the stories of disaster survivors is also of significant interest for improving intervention practices in this context.

ABSTRACT Urbanization is leading to more frequent flooding as cities have more impervious surfaces and runoff exceeds the capacity of combined sewer systems. In heavy rainfall, contaminated excess water is discharged into the natural environment, damaging ecosystems and threatening drinking water sources. To address these challenges aggravated by climate change, urban blue-green water management systems, such as bioretention cells, are increasingly being adopted. Bioretention cells use substrate and plants adapted to the climate to manage rainwater. They form shallow depressions, allowing infiltration, storage, and gradual evacuation of runoff. In 2018, the City of Trois-Rivières (Québec, Canada) installed 54 bioretention cells along a residential street, several of which were equipped with access points to monitor performance. Groundwater quality was monitored through the installation of piezometers to detect potential contamination. This large-scale project aimed to improve stormwater quality and reduce sewer flows. The studied bioretention cells reduced the flow and generally improved water quality entering the sewer system, as well as the quality of stormwater, with some exceptions. Higher outflow concentrations were observed for contaminants such as manganese and nitrate. The results of this initiative provide useful recommendations for similar projects for urban climate change adaptation.

Abstract Real-time precipitation data are essential for weather forecasting, flood prediction, drought monitoring, irrigation, fire prevention, and hydroelectric management. To optimize these activities, reliable precipitation estimates are crucial. Environment and Climate Change Canada (ECCC) leads the Canadian Precipitation Analysis (CaPA) project, providing near-real-time precipitation estimates across North America. However, during winter, CaPA’s 6-hourly accuracy is limited because many automatic surface observations are not assimilated due to wind-induced gauge undercatch. The objective of this study is to evaluate the added value of adjusted hourly precipitation amounts for gauge undercatch due to wind speed in CaPA. A recent ECCC dataset of hourly precipitation measurements from automatic precipitation gauges across Canada is included in CaPA as part of this study. Precipitation amounts are adjusted based on several types of transfer functions, which convert measured precipitation into what high-quality equipment would have measured with reduced undercatch. First, there are no notable differences in CaPA when comparing the performance of the universal transfer function with that of several climate-specific transfer functions based on wind speed and air temperature. However, increasing solid precipitation amounts using a specific type of transfer function that depends on snowfall intensity rather than near-surface air temperature is more likely to improve CaPA’s precipitation estimates during the winter season. This improvement is more evident when the objective evaluation is performed with direct comparison with the Adjusted Daily Rainfall and Snowfall (AdjDlyRS) dataset.

Abstract Public communication about drought and water availability risks poses challenges to a potentially disinterested public. Water management professionals, though, have a responsibility to work with the public to engage in communication about water and environmental risks. Because limited research in water management examines organizational communication practices and perceptions, insights into research and practice can be gained through investigation of current applications of these risk communication efforts. Guided by the CAUSE model, which explains common goals in communicating risk information to the public (e.g., creating Confidence, generating Awareness, enhancing Understanding, gaining Satisfaction, and motivating Enactment), semistructured interviews of professionals ( N = 25) employed by Texas groundwater conservation districts were conducted. The interviews examined how CAUSE model considerations factor in to communication about drought and water availability risks. These data suggest that many work to build constituents’ confidence in their districts. Although audiences and constituents living in drought‐prone areas were reported as being engaged with water availability risks and solutions, many district officials noted constituents’ lack of perceived risk and engagement. Some managers also indicated that public understanding was a secondary concern of their primary responsibilities and that the public often seemed apathetic about technical details related to water conservation risks. Overall, results suggest complicated dynamics between officials and the public regarding information access and motivation. The article also outlines extensions of the CAUSE model and implications for improving public communication about drought and water availability risks.

Abstract Predicting floods and droughts is essential to inform the development of policy in water management, climate change adaptation and disaster risk reduction. Yet, hydrological predictions are highly uncertain, while the frequency, severity and spatial distribution of extreme events are further complicated by the increasing impact of human activities on the water cycle. In this commentary, we argue that four main aspects characterizing the complexity of human‐water systems should be explicitly addressed: feedbacks, scales, tradeoffs and inequalities. We propose the integration of multiple research methods as a way to cope with complexity and develop policy‐relevant science. , Plain Language Summary Several governments today claim to be following the science in addressing crises caused by the occurrence of extreme events, such as floods and droughts, or the emergence of global threats, such as climate change and COVID‐19. In this commentary, we show that there are no universal answers to apparently simple questions such as: Do levees reduce flood risk? Do reservoirs alleviate droughts? We argue that the best science we have consists of a plurality of legitimate interpretations and a range of foresights, which can be enriched by integrating multiple disciplines and research methods. , Key Points Accounting for both power relations and cognitive heuristics is key to unravel the interplay of floods, droughts and human societies Flood and drought predictions are complicated by the increasing impact of human activities on the water cycle We propose the integration of multiple research methods as a way to cope with uncertainty and develop policy‐relevant science

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.

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.

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.

En étant nécessaire à la vie humaine, l’eau est également nécessaire au fonctionnement des économies. Pour qu’elle soit utile à la société, l’eau doit être disponible en quantité et en qualité adéquates, caractéristiques qui ne sont pas toujours disponibles dans la nature. Ainsi, trop ou pas assez d’eau entraînerait des inondations ou des sécheresses, tandis qu’une eau contaminée pourrait être le vecteur de maladies contagieuses mortelles, chacun de ces fléaux entraînant des dommages économiques. Cette thèse est organisée en trois chapitres traitant de thématiques liées aux investissements dans les infrastructures d'eau et à la gestion des maladies infectieuses. Le premier chapitre étudie comment les améliorations apportées aux réseaux d’égouts atténuent les impacts économiques des inondations provoquées par la pluie. Pour estimer l’effet causal de ces investissements, ce chapitre utilise un resserrement inattendu du financement fédéral américain en faveur des réseaux d’égouts, à la suite de l’amendement de 1977 à la politique du Clean Water Act. L'analyse empirique combine un nouveau modèle statistique du risque d'inondation induit par la pluie avec des données horaires sur la quantité de pluie dans les comtés et les codes postaux américains de 1996 à 2019. Les résultats indiquent que des investissements plus importants dans les réseaux d'égouts ont conduit à des réductions substantielles des inondations locales. Les bénéfices de ces investissements sont supérieurs à leurs coûts, économisant près de 23 millions de dollars pour le comté moyen. Dans l’ensemble, ces résultats mettent en évidence à quel point la détérioration des infrastructures publiques peut exacerber les conséquences du changement climatique. Le deuxième chapitre étudie le rôle des épidémies locales de maladies infectieuses dans l'adoption de systèmes centralisés d'approvisionnement en eau dans les premières villes américaines au XIXe siècle. À l’aide d’un vaste corpus de données provenant d’archives de journaux de 1800 à 1896, je construis un nouvel indicateur capturant les épidémies de fièvre jaune, de choléra et de fièvre typhoïde au niveau des villes. Les résultats indiquent que (1) les épidémies locales de maladies infectieuses ont entraîné une augmentation du nombre systèmes d'approvisionnement en eau construits par les villes et ont joué un rôle crucial dans la décision de construire environ 12% des ouvrages d’adduction d’eau en activité en 1897 ; (2) La réponse des villes aux épidémies de typhoïde a été deux fois plus importante que celle qui a suivi les épidémies de fièvre jaune ou de choléra. (3) Les entreprises privées ont construit davantage de nouveaux réseaux d’adduction d’eau après les épidémies locales, tandis que les gouvernements locaux ont procédé à davantage d’améliorations et d’extensions des réseaux d’adduction d’eau publics existants ainsi qu’à des rachats de sociétés d’eau privées. Enfin, je discute du rôle potentiel de divers facteurs sociodémographiques. Le troisième chapitre étudie les coûts économiques associés à une stratégie utilisée pour gérer les épidémies locales lors de la récente pandémie de COVID-19. Dans ce travail en collaboration avec Jian Tang, nous quantifions les effets de la politique ‘zéro-COVID’ à l’aide d’un riche ensemble de données sur les confinements au niveau des comtés en Chine et d’images satellitaires nocturnes. Nous constatons que des confinements plus stricts induisent une forte baisse de la luminosité nocturne au cours de la même période, suivie d’une lente reprise, qui se produit au moins deux trimestres après l’instauration du confinement. En l’absence de contagions généralisées, un comté soumis à un confinement total subit en moyenne une perte de PIB de 6% par rapport aux comtés non confinés. L’effet négatif est particulièrement persistant dans les zones où la production est dominée par les services, par opposition aux zones où la production est dominée par l’activité manufacturière. L’on note par ailleurs la présence d’effets d’entraînement à proximité des comtés confinés, mais ces effets sont de courte durée.

Les variabilités et changements climatiques et les incapacités pour faire face à leurs risques, à leurs effets et, plus précisément, à gérer les catastrophes hydrométéorologiques (inondation et sécheresse) qui les accompagnent, viennent en ajouter aux vulnérabilités et aux problèmes, qui sont déjà une préoccupation en Afrique Subsaharienne et au Bénin. Face à leurs manifestations de plus en plus récurrentes – la faiblesse des systèmes de financement local de la gestion des catastrophes et le déficit des systèmes de protection sociale, qui témoignent des limites des capacités de transfert des risques de catastrophe – cette étude a identifié la structure (gouvernance-ressources), comme le problème essentiel de la gestion des catastrophes au Bénin. Une étude synthétique, étude de cas multiples avec trois niveaux d’analyse imbriqués, dans une approche qualitative, a permis de mieux comprendre comment, dans un contexte de pauvreté, l’intégration de la micro assurance climatique, modifie la structure, le processus et le résultat de la gestion des catastrophes et assure la performance du système et la résilience des populations. Elle a documenté les différents aspects de la structure et des vulnérabilités des systèmes et des populations et a identifié l’absence d’intégration de la micro assurance climatique aux systèmes de gestion des catastrophes, comme un problème au coeur de la complexité des déterminants de la résilience, aussi confrontée à une autre complexité, celle de la diversité des interconnexions entre les différentes catégories de risques, qui place la santé au coeur de tous les risques. La nécessité d’une gestion holistique du risque global, ou d’une gestion tout risque, telle que retenue par le Cadre d’Action de Hyōgo et le Cadre d’Action de Sendai; et l’importance d’apporter une réponse en accord au contexte et à son profil de risques, qui prend l’option pour la "démocratisation" d’une micro assurance climatique, gouvernée sur la base de fondements idéologiques d’équité et d’efficience, cette recherche a préconisé – pour une gestion plus rationnelle, pertinente, efficace et efficiente des catastrophes – une intégration de trois systèmes : le système de la gestion des catastrophes; le système de protection sociale, y compris celui de la micro assurance climatique, et le système de la santé; tous reconnus outillés pour la gestion des risques. Elle a retenu, qu’une telle approche saurait aussi assurer une gestion efficace du changement qu’induirait l’intégration de la micro assurance climatique à la gestion des catastrophes; de ii même qu’une meilleure utilisation des outils et méthodes de sensibilisation, de prévention, de prévision et d’évaluation des risques et des dommages dont recèlent les pratiques en micro assurance climatique. Elle constate que la réussite de l’intégration de la MAC et son développement sont essentiellement plus déterminés par les acteurs et leurs intérêts, que par les ressources financières, même si elles sont aussi indispensables. Cette recherche préconise qu’à partir de choix de modèles et de modes d’intégration bien étudiés, son intégration ou sa prise en compte dans les différents programmes d’aide et de protection sociale mis en oeuvre au Bénin pourrait être, à travers les subventions de l’État, un moyen de mobilisation de fonds en faveur de son financement et de sa viabilité/durabilité. Ce financement pourra aussi s’appuyer sur les mécanismes traditionnels de financement de l’assurance, de la micro assurance, des changements climatiques et de la réduction des risques de catastrophe au Bénin, en Afrique et dans le monde. C’est pourquoi, en termes de gouvernance, ce travail soutien une restructuration avec une gestion entièrement centrée sur les communes, dans une approche des services de première ligne avec les réseaux de services ; en termes de ressources, il a aussi analysé les conditions et les possibilités de développement d’une micro assurance climatique, qui dépend avant tout de la qualité de la gestion des catastrophes (capacités à réduire les risques et limiter les pertes ou capacités à induire la résilience des systèmes et des populations). Cette approche puise dans les réalités et pratiques endogènes de gestion des catastrophes et surtout de protection sociale ou de transfert de risques ; elle s’inspire des bonnes pratiques d’ailleurs ; elle contribue à instaurer l’équité, comme principe de la gestion intégrée des catastrophes et, au-delà de la résilience, à susciter une convergence des efforts pour l’autonomisation de la structure et des populations, face aux manifestations catastrophiques des inondations et de la sécheresse. Cette recherche pense qu’il faut oser la micro assurance universelle pour la gestion des catastrophes hydrométéorologiques; qu’elle est réalisable ou faisable, même en contexte de pauvreté; et qu’il est aussi possible de combiner micro assurance climatique universelle et assurance médicale universelle, dans une dynamique qui mobilise des approches efficientes et les intérêts.

Les changements climatiques anthropogéniques posent des défis énormes pour toutes les sociétés humaines. Ces défis majeurs mettront à l’épreuve les capacités d’adaptation des États et de ses institutions et des communautés partout dans le monde et devront se résoudre par un élan de solidarité humaine afin d’en atténuer les conséquences. Le Canada connaît déjà un réchauffement climatique important. Le pays a d’ailleurs récemment été touché par des événements climatiques extrêmes : des canicules, des feux de forêt, une sécheresse anormale et des inondations dont l’intensité est prévue d’augmenter avec les changements climatiques anthropogéniques. La province du Québec a quant à elle été touchée par de fortes inondations entre 2017 et 2019. L’objectif principal de la présente étude vise à discuter la manière dont le paradigme écosocial peut faire évoluer le travail social en tant que champ de savoir et d’intervention dans un contexte de changements climatiques. Cette étude s’est appuyée sur des données issues de groupes focus réalisés avec des intervenants suite aux inondations survenues au Québec (2017-2019). Notre analyse vise les interventions réalisées en contexte d’inondations, dans le sud de la province, mise en œuvre par le système de santé. Les données ont été collectées lors d’entrevues de groupe réalisées avec des intervenants psychosociaux et des gestionnaires de CI(U)SSS au courant des mois d’octobre et de novembre 2019. Les thèmes suivants ont émergé des analyses: les caractéristiques des inondations de 2019, les divergences d’opinions vis-à-vis des changements climatiques, l’aide et le soutien apportés durant les inondations et la participation citoyenne. J’insisterai également sur l’exacerbation possible des inégalités sociales dans ce contexte. D’autres thèmes se sont également révélés importants : l’engagement des intervenants psychosociaux, la participation et la décentralisation des décisions politiques. Enfin, mes réflexions porteront sur les conséquences sociales qu’entrainent les inondations et sur les types de pratiques sociales qui s’avèrent pertinentes à l’ère des changements climatiques et dans un contexte d’urgence.

Abstract Low flow conditions are governed by short-to-medium term weather conditions or long term climate conditions. This prompts the question: given climate scenarios, is it possible to assess future extreme low flow conditions from climate data indices (CDIs)? Or should we rely on the conventional approach of using outputs of climate models as inputs to a hydrological model? Several CDIs were computed using 42 climate scenarios over the years 1961–2100 for two watersheds located in Quebec, Canada. The relationship between the CDIs and hydrological data indices (HDIs; 7- and 30-day low flows for two hydrological seasons) were examined through correlation analysis to identify the indices governing low flows. Results of the Mann-Kendall test, with a modification for autocorrelated data, clearly identified trends. A partial correlation analysis allowed attributing the observed trends in HDIs to trends in specific CDIs. Furthermore, results showed that, even during the spatial validation process, the methodological framework was able to assess trends in low flow series from: (i) trends in the effective drought index (EDI) computed from rainfall plus snowmelt minus PET amounts over ten to twelve months of the hydrological snow cover season or (ii) the cumulative difference between rainfall and potential evapotranspiration over five months of the snow free season. For 80% of the climate scenarios, trends in HDIs were successfully attributed to trends in CDIs. Overall, this paper introduces an efficient methodological framework to assess future trends in low flows given climate scenarios. The outcome may prove useful to municipalities concerned with source water management under changing climate conditions.

Background Given the important role that municipalities must play in adapting to climate change, it is more than ever essential to measure their progress in this area. However, measuring municipalities’ adaptation progress presents its share of difficulties especially when it comes to comparing (on similar dimensions and over time) the situation of different municipal entities and to linking adaptation impacts to local actions. Longitudinal studies with recurring indicators could capture changes occurring over time, but the development of such indicators requires great emphasis on methodological and psychometric aspects, such as measurement validity. Therefore, this study aimed to develop and validate an index of adaptation to heatwaves and flooding at the level of municipal urbanists and urban planners. Methods A sample of 139 officers working in urbanism and urban planning for municipal entities in the province of Quebec (Canada) completed an online questionnaire. Developed based on a literature review and consultation of representatives from the municipal sector, the questionnaire measured whether the respondent’s municipal entity did or did not adopt the behaviors that are recommended in the scientific and gray literature to adapt to heatwaves and flooding. Results Results of the various metrological analyses (indicator reliability analysis, first order confirmatory factor analysis, concurrent validity analysis, and nomological validity assessment analysis) confirmed the validity of the index developed to measure progress in climate change adaptation at the municipal level. The first dimension of the index corresponds to preliminary measures that inform and prepare stakeholders for action (i.e., groundwork adaptation initiatives), whereas the second refers to measures that aim to concretely reduce vulnerability to climate change, to improve the adaptive capacity or the resilience of human and natural systems (i.e., adaptation actions). Conclusion The results of a series of psychometric analyses showed that the index has good validity and could properly measure the adoption of actions to prepare for adaptation as well as adaptation actions per se. Municipal and government officials can therefore consider using it to monitor and evaluate adaptation efforts at the municipal level.

Excluding Antarctica and Greenland, 3.8% of the world’s glacier area is concentrated in Chile. The country has been strongly affected by the mega drought, which affects the south-central area and has produced an increase in dependence on water resources from snow and glacier melting in dry periods. Recent climate change has led to an elevation of the zero-degree isotherm, a decrease in solid-state precipitation amounts and an accelerated loss of glacier and snow storage in the Chilean Andes. This situation calls for a better understanding of future water discharge in Andean headwater catchments in order to improve water resources management in glacier-fed populated areas. The present study uses hydrological modeling to characterize the hydrological processes occurring in a glacio-nival watershed of the central Andes and to examine the impact of different climate change scenarios on discharge. The study site is the upper sub-watershed of the Tinguiririca River (area: 141 km2), of which nearly 20% is covered by Universidad Glacier. The semi-distributed Snowmelt Runoff Model + Glacier (SRM+G) was forced with local meteorological data to simulate catchment runoff. The model was calibrated on even years and validated on odd years during the 2008–2014 period and found to correctly reproduce daily runoff. The model was then forced with downscaled ensemble projected precipitation and temperature series under the RCP 4.5 and RCP 8.5 scenarios, and the glacier adjusted using a volume-area scaling relationship. The results obtained for 2050 indicate a decrease in mean annual discharge (MAD) of 18.1% for the lowest emission scenario and 43.3% for the most pessimistic emission scenario, while for 2100 the MAD decreases by 31.4 and 54.2%, respectively, for each emission scenario. Results show that decreasing precipitation lead to reduced rainfall and snowmelt contributions to discharge. Glacier melt thus partly buffers the drying climate trend, but our results show that the peak water occurs near 2040, after which glacier depletion leads to reducing discharge, threatening the long-term water resource availability in this region.