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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.

AbstractEl Niño‐Southern Oscillation (ENSO) is often considered as a source of long‐term predictability for extreme events via its teleconnection patterns. However, given that its characteristic cycle varies from two to 7 years, it is difficult to obtain statistically significant conclusions based on observational periods spanning only a few decades. To overcome this, we apply the global flood risk modeling framework developed by Carozza and Boudreault to an equivalent of 1,600 years of bias‐corrected General Circulation Model outputs. The results show substantial anomalies in flood occurrences and impacts for El Niño and La Niña when compared to the all‐year baseline. We were able to obtain a larger global coverage of statistically significant results than previous studies limited to observational data. Asymmetries in anomalies for both ENSO phases show a larger global influence of El Niño than La Niña on flood hazard and risk.

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.

As Earth's atmospheric temperatures and human populations increase, more people are becoming vulnerable to natural and human-induced disasters. This is particularly true in Central America, where the growing human population is experiencing climate extremes (droughts and floods), and the region is susceptible to geological hazards, such as earthquakes and volcanic eruptions, and environmental deterioration in many forms (soil erosion, lake eutrophication, heavy metal contamination, etc.). Instrumental and historical data from the region are insufficient to understand and document past hazards, a necessary first step for mitigating future risks. Long, continuous, well-resolved geological records can, however, provide a window into past climate and environmental changes that can be used to better predict future conditions in the region. The Lake Izabal Basin (LIB), in eastern Guatemala, contains the longest known continental records of tectonics, climate, and environmental change in the northern Neotropics. The basin is a pull-apart depression that developed along the North American and Caribbean plate boundary ∼ 12 Myr ago and contains &gt; 4 km of sediment. The sedimentological archive in the LIB records the interplay among several Earth System processes. Consequently, exploration of sediments in the basin can provide key information concerning: (1) tectonic deformation and earthquake history along the plate boundary; (2) the timing and causes of volcanism from the Central American Volcanic Arc; and (3) hydroclimatic, ecologic, and geomicrobiological responses to different climate and environmental states. To evaluate the LIB as a potential site for scientific drilling, 65 scientists from 13 countries and 33 institutions met in Antigua, Guatemala, in August 2022 under the auspices of the International Continental Scientific Drilling Program (ICDP) and the US National Science Foundation (NSF). Several working groups developed scientific questions and overarching hypotheses that could be addressed by drilling the LIB and identified optimal coring sites and instrumentation needed to achieve the project goals. The group also discussed logistical challenges and outreach opportunities. The project is not only an outstanding opportunity to improve our scientific understanding of seismotectonic, volcanic, paleoclimatic, paleoecologic, and paleobiologic processes that operate in the tropics of Central America, but it is also an opportunity to improve understanding of multiple geological hazards and communicate that knowledge to help increase the resilience of at-risk Central American communities.

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.

L’estimation du débit en rivières est un paramètre clé pour la gestion des ressources hydriques, la prévention des risques liés aux inondations et la planification des équipements hydroélectriques. Lorsque le débit d’eau est très élevé lors d'évènements extrêmes, les méthodes de jaugeage traditionnelles ne peuvent pas être utilisées. De plus, les stations du réseau hydrométrique sont généralement éparses et leur répartition spatiale n’est pas optimale. Par conséquent, de nombreuses sections de rivières ne peuvent être suivies par des mesures et observations du débit. Pour ces raisons, pendant la dernière décennie, les capteurs satellitaires ont été considérés comme une source d’observation complémentaire aux observations traditionnelles du niveau d’eau et du débit en rivières. L’utilisation d’une telle approche a fourni un moyen de maintenir et d’étendre le réseau d'observation hydrométrique. L’approche avec télédétection permet d’estimer le débit à partir des courbes de tarage qui met en relation le débit instantané (Q) et la géométrie d’une section transversale du chenal (la largeur ou la profondeur effective de la surface d’eau). En revanche, cette méthode est associée à des limitations, notamment, sa dépendance aux courbes de tarage. En effet, en raison de leurs natures empiriques, les courbes de tarage sont limitées à des sections spécifiques et ne peuvent être appliquées dans d’autres rivières. Récemment, des techniques d’apprentissage profond ont été appliquées avec succès dans de nombreux domaines, y compris en hydrologie. Dans le présent travail, l’approche d’apprentissage profond a été choisie, en particulier les réseaux de neurones convolutifs (CNN), pour estimer le débit en rivière. L’objectif principal de ce travail est de développer une approche d’estimation du débit en rivières à partir de l’imagerie RADARSAT 1&amp;2 à l’aide de l’apprentissage profond. La zone d’étude se trouve dans l’ecozone du bouclier boréal à l’Est du Canada. Au total, 39 sites hydrographiques ont fait l’objet de cette étude. Dans le présent travail, une nouvelle architecture de CNN a été a été proposée, elle s'adapte aux données utilisées et permet d’estimer le débit en rivière instantané. Ce modèle donne un résultat du coefficient de détermination (R²) et de Nash-Sutcliffe égale à 0.91, le résultat d’erreur quadratique moyenne égale à 33 m³ /s. Cela démontre que le modèle CNN donne une solution appropriée aux problèmes d’estimation du débit avec des capteurs satellites sans intervention humaine. <br /><br />Estimating river flow is a key parameter for effective water resources management, flood risk prevention and hydroelectric facilities planning. In cases of very high flow of water or extreme events, traditional gauging methods cannot be reliable. In addition, hydrometric network stations are often sparse and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. For these reasons, satellite sensors are considered as a complementary observation source to traditional water level and flow observations in the last decades. The use of this kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate the instantaneous flow (Q) to the geometry of a channel cross-section (the effective width or depth of the water surface). On the other hand, remote sensing is also associated with limitations, notably its dependence on the rating curves. Indeed, due to their empirical nature, rating curves are limited to specific sections and cannot be applied in other rivers. Recently, deep learning techniques have been successfully applied in many fields, including hydrology. In the present work, the deep learning approach has been chosen, in particular convolutional neural networks (CNN), to estimate river flow. The main objective of this work is to develop an approach to estimate river flow from RADARSAT 1&amp;2 imagery using deep learning. In this study, 39 hydrographic sites of the Boreal Shield ecozone in Eastern Canada were considered. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. The achieved results demonstrated a coefficient of determination (R²) and Nash-Sutcliffe values of 0.91, and a root mean square error of 33m³ /s. This indicates the effectiveness of CNN in automatic flow estimation with satellite sensors.

Abstract The highly fissile lithology of the rockwalls and the diversity of mass‐wasting processes provide a specific character to the active talus slopes of the northern Gaspé Peninsula since deglaciation. At a regional scale, the geology of the rockwalls, the patterns and modalities of deglaciation and the evolution towards a cold temperate morphoclimatic regime in a maritime context still influence the geomorphological dynamics of scree slopes today. At a local scale, the south–north orientation of the main coastal valleys influences insolation and exposure to prevailing winds, which in turn influence the snow cover regime and the occurrence of freeze–thaw cycles. The statistical analyses carried out from the mapping of 43 talus slopes and their geometric variables allowed the identification of significant environmental factors for the characterization of the dominant geomorphic processes: snow avalanches, frost‐coasted clast flows, debris flows and rockfalls. Slope aspect appears to be a key parameter in the nature of the processes acting on the talus slopes. East‐ and north‐facing talus slopes are generally covered by a significant snowpack in winter and the dominant processes are snow avalanches and debris flows. West‐ and south‐facing talus slopes face prevailing winds and insolation and are subject to frost‐coated clast flows, the main driver for forest regression, and rockfalls. However, the evolution of scree slopes in forested environments remains extremely complex due to the multiscale components that affect their evolution in the short, medium and long term.

Natural disasters have been demonstrated to cause devastating effects on economic and social development in China, but little is known about the relationship between natural disasters and income at the household level. This study explores the impact of natural disasters on household income, expenditure, and inequality in China as the first study of this nature for the country. The empirical analysis is conducted based on a unique panel dataset that contains six waves of the Chinese Household Income Project (CHIP) survey data over the 1988–2018 period, data on natural disasters, and other social and economic status of households. By employing the fixed effects models, we find that disasters increase contemporaneous levels of income inequality, and disasters that occurred in the previous year significantly increase expenditure inequality. Natural disasters increase operating income inequality but decrease transfer income inequality. Poor households are found to be more vulnerable to disasters and suffer significant income losses. However, there is no evidence to suggest that natural disasters significantly reduce the income of upper- and middle-income groups. These findings have important implications for policies aimed at poverty alleviation and revenue recycling, as they can help improve economic justice and enhance resilience to natural disasters.

Abstract Climatic disaster impacts, such as loss of human life as its most severe consequence, have been rising globally. Several studies argue that population growth is responsible for the rise, and the role of climate change is not evident. While disaster mortality is highest in low-income countries, existing studies focus mostly on developed countries. Here we address this impact attribution question in the context of the Global South using disaster-specific mixed-effects regression models. We show that the rise in landslide and flood mortality in a low-income country such as Nepal between 1992-2021 is primarily attributable to increased precipitation extremes. An increase in one standardized unit in maximum one-day precipitation increases flood mortality by 33%, and heavy rain days increase landslide mortality by 45%. Similarly, a one-unit increase in per capita income decreases landslide and flood mortality by 30% and 45%, respectively. Population density does not show significant effects.

What role can a speculative political ecology play in (re)imaging urban futures of climate extremes? In recent years, narratives of dystopian futures of climate extremes have proliferated in geosciences, and across the media and creative arts. These anxiety-fueled narratives often generate a sense of resignation and unavoidability, which contributes to foreclosing the possibility of radically different political projects. In this article, we argue that these narratives conceal the coproduction of nature and society and treat nature as the problem, thereby locking futures into dystopic configurations. Political ecology scholarship can contribute to generate a politics of possibility by reconceptualizing the relations that constitute urban futures under climate extremes as socionatural. This, we argue, calls for a more experimental political ecology and new forms of theorizing. To this aim, we develop a speculative political ecological approach grounded on a numerical model that examines the potential of transformative change in the aftermath of extreme flood events in a capitalist city. Analytically, this opens a unique possibility of exploring urban futures beyond current trajectories, and how these alternative futures might transform vulnerability and inequality across urban spaces. From a policy perspective, we lay the foundations for a new generation of models that apprehend the role of power and agency in shaping uneven urban futures of climate extremes.

Wetlands and reservoirs are important water flow and storage regulators in a river basin; therefore, they can play a crucial role in mitigating flood and hydrological drought risks. Despite the advancement of river basin theory and modeling, our knowledge is still limited about the extent to which these two regulators could perform such a role, especially under future climate extremes. To improve our understanding, we first coupled wetlands and reservoir operations into a semi-spatially explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. The projection of future floods and hydrological droughts was performed using the hydrological model during different periods (near future: 2026–2050, middle century: 2051–2075, and end century: 2076–2100) under five future climate change scenarios. We found that the risk of future floods and hydrological droughts can vary across different periods – in particular, it will experience relatively large increases and slight decreases. This large river basin will experience flood events of longer duration, with larger peak flows and volume, and of enhanced flashiness compared to the historical period. Simultaneously, the hydrological droughts will be much more frequent, with longer durations and more serious deficits. Therefore, the risk of floods and droughts will, overall, increase further under future climate change even under the combined influence of reservoirs and wetlands. These findings highlight the hydrological regulation function of wetlands and reservoirs and attest that the combining of wetlands with reservoir operation cannot fully eliminate the increasing future flood and drought risks. To improve a river basin's resilience to the risks of future climate change, we argue that the implementation of wetland restoration and the development of accurate forecasting systems for effective reservoir operation are of great importance. Furthermore, this study demonstrated a wetland–reservoir integrated modeling and assessment framework that is conducive to risk assessment of floods and hydrological droughts and that can be used for other river basins in the world.

Numerous government and non-governmental agencies are increasing their efforts to better quantify the disproportionate effects of climate risk on vulnerable populations with the goal of creating more resilient communities. Sociodemographic based indices have been the primary source of vulnerability information the past few decades. However, using these indices fails to capture other facets of vulnerability, such as the ability to access critical resources (e.g., grocery stores, hospitals, pharmacies, etc.). Furthermore, methods to estimate resource accessibility as storms occur (i.e., in near-real time) are not readily available to local stakeholders. We address this gap by creating a model built on strictly open-source data to solve the user equilibrium traffic assignment problem to calculate how an individual's access to critical resources changes during and immediately after a flood event. Redundancy, reliability, and recoverability metrics at the household and network scales reveal the inequitable distribution of the flood's impact. In our case-study for Austin, Texas we found that the most vulnerable households are the least resilient to the impacts of floods and experience the most volatile shifts in metric values. Concurrently, the least vulnerable quarter of the population often carries the smallest burdens. We show that small and moderate inequalities become large inequities when accounting for more vulnerable communities' lower ability to cope with the loss of accessibility, with the most vulnerable quarter of the population carrying four times as much of the burden as the least vulnerable quarter. The near-real time and open-source model we developed can benefit emergency planning stakeholders by helping identify households that require specific resources during and immediately after hazard events.

Economic inequality is rising within many countries globally, and this can significantly influence the social vulnerability to natural hazards. We analysed income inequality and flood disasters in 67 middle- and high-income countries between 1990 and 2018 and found that unequal countries tend to suffer more flood fatalities. This study integrates geocoded mortality records from 573 major flood disasters with population and economic data to perform generalized linear mixed regression modelling. Our results show that the significant association between income inequality and flood mortality persists after accounting for the per-capita real gross domestic product, population size in flood-affected regions and other potentially confounding variables. The protective effect of increasing gross domestic product disappeared when accounting for income inequality and population size in flood-affected regions. On the basis of our results, we argue that the increasingly uneven distribution of wealth deserves more attention within international disaster-risk research and policy arenas.

Abstract This work explores the relationship between catchment size, rainfall duration, and future streamflow increases on 133 North American catchments with sizes ranging from 66.5 to 9886 km2. It uses the outputs from a high spatial (0.11°) and temporal (1-h) resolution single model initial-condition large ensemble (SMILE) and a hydrological model to compute extreme rainfall and streamflow for durations ranging from 1 to 72 h and for return periods of between 2 and 300 years. Increases in extreme precipitation are observed across all durations and return periods. The projected increases are strongly related to duration, frequency, and catchment size, with the shortest durations, longest return periods, and smaller catchments witnessing the largest relative rainfall increases. These increases can be quite significant, with the 100-yr rainfall becoming up to 20 times more frequent over the smaller catchments. A similar duration–frequency–size pattern of increases is also observed for future extreme streamflow, but with even larger relative increases. These results imply that future increases in extreme rainfall will disproportionately impact smaller catchments, and particularly so for impervious urban catchments which are typically small, and whose stormwater drainage infrastructures are designed for long-return-period flows, both being conditions for which the amplification of future flow will be maximized.

The Peace–Athabasca Delta (PAD) in western Canada is one of the largest inland deltas in the world. Flooding caused by the expansion of lakes beyond normal shorelines occurred during the summer of 2020 and provided a unique opportunity to evaluate the capabilities of remote sensing platforms to map surface water expansion into vegetated landscape with complex surface connectivity. Firstly, multi-source remotely sensed data via satellites were used to create a temporal reconstruction of the event spanning May to September. Optical synthetic aperture radar (SAR) and altimeter data were used to reconstruct surface water area and elevation as seen from space. Lastly, temporal water surface area and level data obtained from the existing satellites and hydrometric stations were used as input data in the CNES Large-Scale SWOT Simulator, which provided an overview of the newly launched SWOT satellite ability to monitor such flood events. The results show a 25% smaller water surface area for optical instruments compared to SAR. Simulations show that SWOT would have greatly increased the spatio-temporal understanding of the flood dynamics with complete PAD coverage three to four times per month. Overall, seasonal vegetation growth was a major obstacle for water surface area retrieval, especially for optical sensors.

The convection-permitting climate model (CPCM), WRF-ARW at 4 km resolution, is able to capture the observed relationships between precipitation extremes and temperature (PT scaling) in western Canada. By analyzing the CPCM simulated PT scalings, we found they have robust patterns at different percentiles of precipitation intensity and even between the current and future climate. This is due to the stable annual cycle of the regional climate. The PT scaling pattern is physically governed by the amount of water vapour and the ascending velocity of air. Approximately 95% of the precipitation intensity variation can be explained by the vertical velocity and precipitable water in western Canada. The PT scaling for the current climate does not tell how precipitation extremes would response to a warmer climate. Trend scaling theory was utilized to estimate the intensification of precipitation extremes in a warmer climate. It shows that, in western Canada, the coast is particularly vulnerable to precipitation extremes under global warming. Precipitation extremes are projected to increase at a super Clausius-Clapeyron (CC) scale over the coast, approximately at a CC scale over the prairies and mountains, and a sub-CC scale over the northern region. The warming effect on precipitation extremes is even stronger when the concept of”wet-day trend scaling” is introduced.

In the context of global warming, the Clausius–Clapeyron (CC) relationship has been widely used as an indicator of the evolution of the precipitation regime, including daily and sub-daily extremes. This study aims to verify the existence of links between precipitation extremes and 2 m air temperature for the Ottawa River Basin (ORB, Canada) over the period 1981–2010, applying an exponential relationship between the 99th percentile of precipitation and temperature characteristics. Three simulations of the Canadian Regional Climate Model version 5 (CRCM5), at three different resolutions (0.44°, 0.22°, and 0.11°), one simulation using the recent CRCM version 6 (CRCM6) at “convection-permitting” resolution (2.5 km), and two reanalysis products (ERA5 and ERA5-Land) were used to investigate the CC scaling hypothesis that precipitation increases at the same rate as the atmospheric moisture-holding capacity (i.e., 6.8%/°C). In general, daily precipitation follows a lower rate of change than the CC scaling with median values between 2 and 4%/°C for the ORB and with a level of statistical significance of 5%, while hourly precipitation increases faster with temperature, between 4 and 7%/°C. In the latter case, rates of change greater than the CC scaling were even up to 10.2%/°C for the simulation at 0.11°. A hook shape is observed in summer for CRCM5 simulations, near the 20–25 °C temperature threshold, where the 99th percentile of precipitation decreases with temperature, especially at higher resolution with the CRCM6 data. Beyond the threshold of 20 °C, it appears that the atmospheric moisture-holding capacity is not the only determining factor for generating precipitation extremes. Other factors need to be considered, such as the moisture availability at the time of the precipitation event, and the presence of dynamical mechanisms that increase, for example, upward vertical motion. As mentioned in previous studies, the applicability of the CC scaling should not be generalised in the study of precipitation extremes. The time and spatial scales and season are also dependent factors that must be taken into account. In fact, the evolution of precipitation extremes and temperature relationships should be identified and evaluated with very high spatial resolution simulations, knowing that local temperature and regional physiographic features play a major role in the occurrence and intensity of precipitation extremes. As precipitation extremes have important effects on the occurrence of floods with potential deleterious damages, further research needs to explore the sensitivity of projections to resolution with various air temperature and humidity thresholds, especially at the sub-daily scale, as these precipitation types seem to increase faster with temperature than with daily-scale values. This will help to develop decision-making and adaptation strategies based on improved physical knowledge or approaches and not on a single assumption based on CC scaling.