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Climate change is likely to affect windthrow risks at northern latitudes by potentially changing high wind probabilities and soil frost duration. Here, we evaluated the effect of climate change on windthrow risk in eastern Canada’s balsam fir (Abies balsamea [L.] Mill.) forests using a methodology that accounted for changes in both wind speed and soil frost duration. We used wind speed and soil temperature projections at the regional scale from the CRCM5 regional climate model (RCM) driven by the CanESM2 global climate model (GCM) under two representative concentration pathways (RCP4.5, RCP8.5), for a baseline (1976–2005) and two future periods (2041–2070, 2071–2100). A hybrid mechanistic model (ForestGALES) that considers species resistance to uprooting and wind speed distribution was used to calculate windthrow risk. An increased risk of windthrow (3 to 30%) was predicted for the future mainly due to an increased duration of unfrozen soil conditions (by up to 2 to 3 months by the end of the twenty-first century under RCP8.5). In contrast, wind speed did not vary markedly with a changing climate. Strong regional variations in wind speeds translated into regional differences in windthrow risk, with the easternmost region (Atlantic provinces) having the strongest winds and the highest windthrow risk. Because of the inherent uncertainties associated with climate change projections, especially regarding wind climate, further research is required to assess windthrow risk from the optimum combination of RCM/GCM ensemble simulations.

Abstract Retrospective estimation of daily streamflow for all rivers within a territory is of practical interest for sustainable and optimal water management. This implies, however, the availability of methods for providing accurate estimations of flow for ungauged rivers. This study compares the potential of statistical interpolation (SI)—a simple data assimilation technique that combines observations and simulations from hydrological modelling—with four other approaches: nearest neighbour, direct use of outputs from hydrological modelling, ordinary and topological kriging. Through subsampling cross-validation analyses based on the modified Kling-Gupta efficiency indicator, we show that SI compares favourably with these other approaches. While the performance of other methods depends on the configuration of the ungauged site in regards to the neighbouring reference sites, SI is less affected by these configurations. SI outperforms the other approaches particularly where the ungauged site is relatively distant from observation sites. In these cases, SI performance depends on the performance of the background model that relies on simulations of hydrological processes forced by precipitation and temperature observations. Our findings offer the potential for heightened performance estimates through an improvement of hydrological modelling and the use of more complex assimilation techniques for exploiting the model.

Several businesses and industries rely on rainfall forecasts to support their day-to-day operations. To deal with the uncertainty associated with rainfall forecast, some meteorological organisations have developed products, such as ensemble forecasts. However, due to the intensive computational requirements of ensemble forecasts, the spatial resolution remains coarse. For example, Environment and Climate Change Canada’s (ECCC) Global Ensemble Prediction System (GEPS) data is freely available on a 1-degree grid (about 100 km), while those of the so-called High Resolution Deterministic Prediction System (HRDPS) are available on a 2.5-km grid (about 40 times finer). Potential users are then left with the option of using either a high-resolution rainfall forecast without uncertainty estimation and/or an ensemble with a spectrum of plausible rainfall values, but at a coarser spatial scale. The objective of this study was to evaluate the added value of coupling the Gibbs Sampling Disaggregation Model (GSDM) with ECCC products to provide accurate, precise and consistent rainfall estimates at a fine spatial resolution (10-km) within a forecast framework (6-h). For 30, 6-h, rainfall events occurring within a 40,000-km2 area (Quebec, Canada), results show that, using 100-km aggregated reference rainfall depths as input, statistics of the rainfall fields generated by GSDM were close to those of the 10-km reference field. However, in forecast mode, GSDM outcomes inherit of the ECCC forecast biases, resulting in a poor performance when GEPS data were used as input, mainly due to the inherent rainfall depth distribution of the latter product. Better performance was achieved when the Regional Deterministic Prediction System (RDPS), available on a 10-km grid and aggregated at 100-km, was used as input to GSDM. Nevertheless, most of the analyzed ensemble forecasts were weakly consistent. Some areas of improvement are identified herein.

En marge de la Cinquième Plateforme régionale pour la Réduction des risques de catastrophes des Amériques (PRA), le gouvernement du Canada a approché l’Institut des sciences de l’environnement(ISE) de l’Université du Québec à Montréal(UQAM) afin d’organiser un forum public. Les échanges de ce dernier devaient servir à alimenter les discussions de la PRA. Au total, 21 experts ont discuté avec une centaine de participants lors de panels organisés à l’UQAM sous les thèmes de la santé, de la sécurité civile et de l’aménagement du territoire. Plusieurs thèmes transversaux ont aussi émergé tout au long du forum. Il importe de pérenniser le rôle de la recherche et d’améliorer les capacités de formation technique et universitaire afin de former des spécialistes en mesure d’appréhender la complexité de la gestion du risque dans un contexte de changements environnementaux et climatiques. Ceci est également essentiel pour l’identification des facteurs de risque (multisources ou multidimensionnels), pour tirer des leçons apprises des événements majeurs passés et récents, et pour développer ou mettre à jour la connaissance sur les tendances en cours et à venir des aléas météorologiques, ainsi que des facteurs de vulnérabilité et d’exposition. Tous les panels ont discuté de l’importance de favoriser le décloisonnement intra/interorganisationnel pour promouvoir la transsectorialité et les retours d’expériences systématiques. Pour ce faire, il faut s’inspirer des modèles internationaux, notamment du système d’alertes hydrométéorologiques présenté par Météo-France. Celui-ci inclut une vigilance météorologique qui cible des populations et des autorités publiques, et les informe des comportements et des règles à suivre lors d’alertes plus problématiques (vigilance aux stades orange et rouge). Finalement, l’amélioration de la communication et le libre accès à l’information sont des éléments essentiels pour protéger les individus et développer une société plus résiliente.

Snow avalanches are a major natural hazard for road users and infrastructure in northern Gaspesie. Over the past 11 years, the occurrence of nearly 500 snow avalanches on the two major roads servicing the area was reported. No management program is currently operational. In this study, we analyze the weather patterns promoting snow avalanche initiation and use logistic regression (LR) to calculate the probability of avalanche occurrence on a daily basis. We then test the best LR models over the 2012–2013 season in an operational forecasting perspective: Each day, the probability of occurrence (0–100%) determined by the model was classified into five classes avalanche danger scale. Our results show that avalanche occurrence along the coast is best predicted by 2 days of accrued snowfall [in water equivalent (WE)], daily rainfall, and wind speed. In the valley, the most significant predictive variables are 3 days of accrued snowfall (WE), daily rainfall, and the preceding 2 days of thermal amplitude. The large scree slopes located along the coast and exposed to strong winds tend to be more reactive to direct snow accumulation than the inner-valley slopes. Therefore, the probability of avalanche occurrence increases rapidly during a snowfall. The slopes located in the valley are less responsive to snow loading. The LR models developed prove to be an efficient tool to forecast days with high levels of snow avalanche activity. Finally, we discuss how road maintenance managers can use this forecasting tool to improve decision making and risk rendering on a daily basis.

Abstract Urban political ecology (UPE) has mainly evolved within the discipline of geography to examine the power relations that produce uneven urban spaces (infrastructures and natures) and unequal access to resources in cities. Its increasingly poststructuralist orientation demands the questioning of received categories and concepts, including those of (neoliberal) governance, government, and of the state. This paper attempts to open this black box by referring to the mostly anthropological literature on everyday governance and the everyday state. We argue that UPE could benefit from ethnographic governance studies to unveil multiple state and non‐state actors that influence the local environment, their diverse rationalities, normative registers, and interactions across scales. This would also to enrich and nuance geographical UPE accounts of neoliberal environmental governance and potentially render the framework more policy relevant.

This paper examines the extent to which economic development decreases a country's risk of experiencing climate-related disasters as well as the societal impacts of those events. The paper proceeds from the underlying assumption that disasters are not inherently natural, but arise from the intersection of naturally-occurring hazards within fragile environments. It uses data from the International Disaster Database (EM-DAT),(1) representing country-year-level observations over the period 1980-2007. The study finds that low-income countries are significantly more at risk of climate-related disasters, even after controlling for exposure to climate hazards and other factors that may confound disaster reporting. Following the occurrence of a disaster, higher income generally diminishes a country's social vulnerability to such happenings, resulting in lower levels of mortality and morbidity. This implies that continued economic development may be a powerful tool for lessening social vulnerability to climate change.© 2016 The Author(s). Disasters © Overseas Development Institute, 2016. Language: en