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Abstract This paper focuses on evaluating the uncertainty of three common regionalization methods for predicting continuous streamflow in ungauged basins. A set of 268 basins covering 1.6 million km 2 in the province of Quebec was used to test the regionalization strategies. The multiple linear regression, spatial proximity, and physical similarity approaches were evaluated on the catchments using a leave‐one‐out cross‐validation scheme. The lumped conceptual HSAMI hydrological model was used throughout the study. A bootstrapping method was chosen to further estimate uncertainty due to parameter set selection for each of the parameter set/regionalization method pairs. Results show that parameter set selection can play an important role in regionalization method performance depending on the regionalization methods (and their variants) used and that equifinality does not contribute significantly to the overall uncertainty witnessed throughout the regionalization methods applications. Regression methods fail to consistently assign behavioral parameter sets to the pseudoungauged basins (i.e., the ones left out). Spatial proximity and physical similarity score better, the latter being the best. It is also shown that combining either physical similarity or spatial proximity with the multiple linear regression method can lead to an even more successful prediction rate. However, even the best methods were shown to be unreliable to an extent, as successful prediction rates never surpass 75%. Finally, this paper shows that the selection of catchment descriptors is crucial to the regionalization strategies' performance and that for the HSAMI model, the optimal number of donor catchments for transferred parameter sets lies between four and seven. , Key Points Uncertainty can be limited in regionalization Physical similarity method is best, followed by spatial proximity Regression‐augmented methods can yield better performance

La connaissance de l’aléa inondation est un enjeu croissant pour la gestion du risque inondation. En France, afin de répondre à la demande des pouvoirs publics de cartographier les zones inondables, la cartographie hydrogéomorphologique émerge. Cette approche naturaliste, développée durant les années 1980, se propagea dans une large partie du Sud de la France. Aujourd’hui, la cartographie hydrogéomorphologique est une méthode reconnue pour sa fiabilité, mais elle stagne en termes d’applications et de développement dans de nouveaux territoires. Afin d’améliorer la connaissance de la prise en compte de la cartographie hydrogéomorphologique dans un cadre règlementaire, une analyse de 282 PPRI a été menée. Cette analyse a permis de dégager les modalités de prise en compte de la cartographie hydrogéomorphologique, mais également d’émettre des hypothèses sur les raisons de leur développement. La réalisation de MNZI (Modèle Numériques de Zones Inondables) sur des cours d’eau français et canadiens, a permis de mettre en avant l’adaptabilité de la cartographie hydrogéomorphologique à des contextes physiques différents, mais aussi d’apporter une réponse à la demande de prise en compte de cartographie de crue extrême de la Directive Inondation. L’évolution cartographique proposée dans ce travail comprend une réflexion autour de la légende des AZI, mais aussi sur le concept de cartographie positive. A travers les aspects cartographiques, règlementaires et géomorphologiques abordés, nous finissons par proposer un modèle de développement de la cartographie hydrogéomorphologique.

Wetlands play an important role in preventing extreme low flows in rivers and groundwater level drawdowns during drought periods. This hydrological function could become increasingly important under a warmer climate. Links between peatlands, aquifers, and rivers remain inadequately understood. The objective of this study was to evaluate the hydrologic functions of the Lanoraie peatland complex in southern Quebec, Canada, under different climate conditions. This peatland complex has developed in the beds of former fluvial channels during the final stages of the last deglaciation. The peatland covers a surface area of ~76 km2 and feeds five rivers. Numerical simulations were performed using a steady-state groundwater flow model. Results show that the peatland contributes on average to 77% of the mean annual river base flow. The peatland receives 52% of its water from the aquifer. Reduced recharge scenarios (−20 and −50% of current conditions) were used as a surrogate of climate change. With these scenarios, the simulated mean head decreases by 0.6 and 1.6 m in the sand. The mean river base flow decreases by 16 and 41% with the two scenarios. These results strongly underline the importance of aquifer-peatland-river interactions at the regional scale. They also point to the necessity of considering the entire hydrosystem in conservation initiatives.

Since the 1980s, populations of the Asian tiger mosquito Aedes albopictus have become established in south-eastern, eastern and central United States, extending to approximately 40°N. Ae. albopictus is a vector of a wide range of human pathogens including dengue and chikungunya viruses, which are currently emerging in the Caribbean and Central America and posing a threat to North America.

This study analyzes the uncertainty of seasonal (winter and summer) precipitation extremes as simulated by a recent version of the Canadian Regional Climate Model (CRCM) using 16 simulations (1961–1990), considering four sources of uncertainty from: (a) the domain size, (b) the driving Atmosphere–Ocean Global Climate Models (AOGCM), (c) the ensemble member for a given AOGCM and (d) the internal variability of the CRCM. These 16 simulations are driven by 2 AOGCMs (i.e. CGCM3, members 4 and 5, and ECHAM5, members 1 and 2), and one set of re-analysis products (i.e. ERA40), using two domain sizes (AMNO, covering all North America and QC, a smaller domain centred over the Province of Québec). In addition to the mean seasonal precipitation, three seasonal indices are used to characterize different types of variability and extremes of precipitation: the number of wet days, the maximum number of consecutive dry days, and the 95th percentile of daily precipitation. Results show that largest source of uncertainty in summer comes from the AOGCM selection and the choice of domain size, followed by the choice of the member for a given AOGCM. In winter, the choice of the member becomes more important than the choice of the domain size. Simulated variance sensitivity is greater in winter than in summer, highlighting the importance of the large-scale circulation from the boundary conditions. The study confirms a higher uncertainty in the simulated heavy rainfall than the one in the mean precipitation, with some regions along the Great Lakes—St-Lawrence Valley exhibiting a systematic higher uncertainty value.

Significant advancements have been made in examining the relationship between economic development and disaster losses at the global and national scales, but very little research has been done at the sub-national level, especially in China. Based on socioeconomic and disaster impact data from 31 provinces (municipalities, and autonomous regions) in China from 1990 to 2010, ordinary least squares regression was used to determine the relationship between socioeconomic development and effects of natural disasters. Results showed that economic development played a distinct role in mitigating disaster damages in the whole China and its eastern, central and western regions. There existed a U-shaped relationship between economic growth and disaster losses in the whole China and its eastern region, and an inverted-U nonlinearity linkage in its central and western areas. These findings further confirmed the existence of a nonlinear relationship between economic development and disaster losses. Economic growth had played a more important role in mitigating disaster losses in the central region of China than that in the western one. Further investigations demonstrated that as economic develops, there were fewer deaths caused by natural hazards in whole China and all its three regions. The combination of the lower level of education, higher unemployment rate and greater gross dependence ratio has contributed to the increase in death toll caused by natural disasters, but this trend could be partly offset by wealth growth.

Renforcer la capacité d’intervention et d’adaptation en santé publique nécessite d’améliorer l’efficacité des systèmes d’alerte précoce vis-à-vis des risques climatiques en évolution. Ceci implique des ajustements aux activités en cours, voire de modifier les façons de faire au sein des organisations et entre les organisations en augmentant, notamment, leurs collaborations. L’interdisciplinarité au service de la santé publique est donc de mise.

AbstractAlthough environmental justice (EJ) research in the United States has traditionally focused on technological hazards such as air pollution or hazardous waste, the adverse and unequal impacts of Hurricane Katrina have prompted researchers to examine the EJ implications of natural events such as hurricanes and floods. This paper contributes to this emerging literature on EJ and social vulnerability to natural hazards by analyzing racial/ethnic and socioeconomic inequities in the distribution of flood risk exposure in the Miami Metropolitan Statistical Area (MSA), Florida—one of the most hurricane-prone areas in the world and one of the most ethnically and socioeconomically diverse MSAs in the United States. Although previous studies have relied exclusively on the 100-year floodplain to assess the spatial extent of flood exposure, this study makes a systematic distinction between different types of flood zones on the basis of both the probability (100-year versus 500-year versus low/no risk) of flood...

Disasters such as floods, storms, heatwaves and droughts can have enormous implications for health, the environment and economic development. In this article, we address the question of how climate change might have influenced the impact of weather-related disasters. This relation is not straightforward, since disaster burden is not influenced by weather and climate events alone—other drivers are growth in population and wealth, and changes in vulnerability. We normalized disaster impacts, analyzed trends in the data and compared them with trends in extreme weather and climate events and vulnerability, following a 3 by 4 by 3 set-up, with three disaster burden categories, four regions and three extreme weather event categories. The trends in normalized disaster impacts show large differences between regions and weather event categories. Despite these variations, our overall conclusion is that the increasing exposure of people and economic assets is the major cause of increasing trends in disaster impacts. This holds for long-term trends in economic losses as well as the number of people affected. We also found similar, though more qualitative, results for the number of people killed; in all three cases, the role played by climate change cannot be excluded. Furthermore, we found that trends in historic vulnerability tend to be stable over time, despite adaptation measures taken by countries. Based on these findings, we derived disaster impact projections for the coming decades. We argue that projections beyond 2030 are too uncertain, not only due to unknown changes in vulnerability, but also due to increasing non-stationarities in normalization relations.