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Precipitation and temperature are among major climatic variables that are used to characterize extreme weather events, which can have profound impacts on ecosystems and society. Accurate simulation of these variables at the local scale is essential to adapt urban systems and policies to future climatic changes. However, accurate simulation of these climatic variables is difficult due to possible interdependence and feedbacks among them. In this paper, the concept of copulas was used to model seasonal interdependence between precipitation and temperature. Five copula functions were fitted to grid (approximately 10 km × 10 km) climate data from 1960 to 2013 in southern Ontario, Canada. Theoretical and empirical copulas were then compared with each other to select the most appropriate copula family for this region. Results showed that, of the tested copulas, none of them consistently performed the best over the entire region during all seasons. However, Gumbel copula was the best performer during the winter season, and Clayton performed best in the summer. More variability in terms of best copula was found in spring and fall seasons. By examining the likelihoods of concurrent extreme temperature and precipitation periods including wet/cool in the winter and dry/hot in the summer, we found that ignoring the joint distribution and confounding impacts of precipitation and temperature lead to the underestimation of occurrence of probabilities for these two concurrent extreme modes. This underestimation can also lead to incorrect conclusions and flawed decisions in terms of the severity of these extreme events.
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Abstract Background During Spring 2019, many regions in Quebec (Canada) experienced severe floods. As much as 5,245 households were flooded and 7,452 persons were evacuated, causing extensive material and human damages. A large population-based study was therefore conducted to examine medium-term effects of this natural disaster on health and well-being. Methods Six to eight months post-floods, households located in the flooded zones (in one of the 6 Quebec regions the most severely affected) were randomly invited to participate to a telephone or a web-based survey (response rate=15.3%). Several psychological health outcomes were examined, including psychological distress (based on the 6-item Kessler Scale, score 0-24) and post-traumatic stress (based on the 15-item Impact of Event Scale, score 0-75). These outcomes were compared among 3 levels of exposure using Chi-square test: flooded (floodwater in ≥ 1 liveable room), disrupted (floodwater in non-liveable areas, loss of utilities, loss of access to services, or evacuation), and unaffected. Results Of the 3,437 participating households, 349 (10.2%) were flooded and 1230 (35.8%) were disrupted (but not flooded) during the 2019 floods. A steep gradient was observed for moderate/severe symptoms of post-traumatic stress (score ≥ 26) according to the level of exposure to flooding (unaffected: 3.0%; disrupted: 14.6%; flooded: 44.1%; p < 0.0005). For psychological distress (score ≥ 7), the baseline level (i.e. unaffected group) was 7.3% while it reached 15.0% and 38.4% in the disrupted and the flooded groups, respectively (p < 0.0005). Conclusions This study is among the largest to examine the psychological impacts of flooding. The magnitude of effects observed in flooded households is consistent with the literature and calls for stronger social and economic measures to support flood victims. Such support should help coping with initial stress, but also alleviating secondary stressors classically observed in post-flood settings. Key messages Psychological impacts of floods may persist for several months and may be observed in both flooded and disrupted people. Stronger social and economic measures are needed to better support flood victims, not only in the short but also in the longer term.
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The paper describes the development of predictive equations of windthrow for five tree species based on remote sensing of wind-affected stands in southwestern New Brunswick (NB). The data characterises forest conditions before, during and after the passing of extratropical cyclone Arthur, July 4–5, 2014. The five-variable logistic function developed for balsam fir (bF) was validated against remote-sensing-acquired windthrow data for bF-stands affected by the Christmas Mountains windthrow event of November 7, 1994. In general, the prediction of windthrow in the area agreed fairly well with the windthrow sites identified by photogrammetry. The occurrence of windthrow in the Christmas Mountains was prominent in areas with shallow soils and prone to localised accelerations in mean and turbulent airflow. The windthrow function for bF was subsequently used to examine the future impact of windthrow under two climate scenarios (RCP’s 4.5 and 8.5) and species response to local changes anticipated with global climate change, particularly with respect to growing degree-days and soil moisture. Under climate change, future windthrow in bF stands (2006–2100) is projected to be modified as the species withdraws from the high-elevation areas and NB as a whole, as the climate progressively warms and precipitation increases, causing the growing environment of bF to deteriorate.