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Abstract A semiparametric copula joint framework was proposed to model wind gust speed (WGS) and maximum temperature (MT) in Canada, using Gaussian kernel density estimation (GKDE) with parametric copulas. Their joint probability estimates allow for a better understanding of the risk of power blackouts and the demand for air conditioning in the community. The bivariate framework used two extreme sample groups to define extreme pairs at different time lags, i.e., 0 to ± 3 days, annual maximum WGS (AMWGS) and corresponding MT and annual highest MT (AHMT) and corresponding WGS. A thorough model performance comparison indicated that GKDE outperformed the parametric models in defining the marginal distribution of selected univariate series. Significant positive correlations were observed among extreme pairs, except for Calgary and Halifax stations, with inconsistent correlation variations based on selected cities and lag time. Various parametric 2-D copulas were selected to model the dependence structure of bivariate pairs at different time lags for selected stations. AMWGS or AHMT events, when considered independently, would be stressful for all stations due to high estimated quantiles with low univariate RPs. The bivariate events exhibited lower AND-joint RPs with moderate to high design quantiles, indicating a higher risk of power blackouts and heightened air-conditioning demands, which varied inconsistently with time lags across the station. The bivariate AMWGS and corresponding MT events would be stressful in Regina, Quebec City, Ottawa, and Edmonton, while AHMT and corresponding WGS events in Toronto, Regina, and Montreal. Conversely, Vancouver poses a lower risk of joint action of pairs AHMT and corresponding WGS events. These hazard statistics can help in better planning for community well-being during extreme weather.

Abstract This study assesses the deterministic and probabilistic forecasting skill of a 1-month-lead ensemble of Artificial Neural Networks (EANN) based on low-frequency climate oscillation indices. The predictand is the February-April (FMA) rainfall in the Brazilian state of Ceará, which is a prominent subject in climate forecasting studies due to its high seasonal predictability. Additionally, the study proposes combining the EANN with dynamical models into a hybrid multi-model ensemble (MME). The forecast verification is carried out through a leave-one-out cross-validation based on 40 years of data. The EANN forecasting skill is compared with traditional statistical models and the dynamical models that compose Ceará’s operational seasonal forecasting system. A spatial comparison showed that the EANN was among the models with the smallest Root Mean Squared Error (RMSE) and Ranked Probability Score (RPS) in most regions. Moreover, the analysis of the area-aggregated reliability showed that the EANN is better calibrated than the individual dynamical models and has better resolution than Multinomial Logistic Regression for above-normal (AN) and below-normal (BN) categories. It is also shown that combining the EANN and dynamical models into a hybrid MME reduces the overconfidence of the extreme categories observed in a dynamically-based MME, improving the reliability of the forecasting system.

Abstract 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.

Abstract Background Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems to mitigate the health consequences of extreme heat events. HHWWS usually focuses on the four hottest months of the year and imposes the same threshold over these months. However, according to climate projections, the warm season is expected to extend and/or shift. Some studies demonstrated that health impacts of heat waves are more severe when the human body is not acclimatized to the heat. In order to adapt those systems to potential heat waves occurring outside the hottest months of the season, this study proposes specific health-based monthly heat indicators and thresholds over an extended season from April to October in the northern hemisphere. Methods The proposed approach, an adoption and extension of the HHWWS methodology currently implemented in Quebec (Canada). The latter is developed and applied to the Greater Montreal area (current population 4.3 million) based on historical health and meteorological data over the years. This approach consists of determining excess mortality episodes and then choosing monthly indicators and thresholds that may involve excess mortality. Results We obtain thresholds for the maximum and minimum temperature couple (in °C) that range from (respectively, 23 and 12) in April, to (32 and 21) in July and back to (25 and 13) in October. The resulting HHWWS is flexible, with health-related thresholds taking into account the seasonality and the monthly variability of temperatures over an extended summer season. Conclusions This adaptive and more realistic system has the potential to prevent, by data-driven health alerts, heat-related mortality outside the typical July–August months of heat waves. The proposed methodology is general and can be applied to other regions and situations based on their characteristics.

The coasts and islands that flank Abu Dhabi, the United Arab Emirates (UAE)’s largest emirate, host the country’s most significant coastal and marine habitats including coral reefs. These reefs, although subject to a variety of pressures from urban and industrial encroachment and climate change, exhibit the highest thresholds for coral bleaching and mortality in the world. By reviewing and benchmarking global, regional and local coral reef conservation efforts, this study highlights the ecological importance and economic uniqueness of the UAE corals in light of the changing climate. The analysis provides a set of recommendations for coral reef management that includes an adapted institutional framework bringing together stakeholders, scientists, and managers. These recommendations are provided to guide coral reef conservation efforts regionally and in jurisdictions with comparable environmental challenges.

The warming observed over the past summers since 2000 is unprecedented in climate records in Europe and especially in France. Extreme temperatures and heat spells were often analyzed in the literature by applying extreme value theory but rarely in a non-stationary (NS) framework and duration modeling is often excluded. For a modern risk-based approach, it is important to have knowledge of the duration, magnitude, and frequency of occurrence of heat spells in a climate variability and change context. Yet, despite their obvious importance, teleconnections and associated climate indices (CIs) have often been excluded from heat spell modelling. The notion of duration is also not easily interpretable in a frequency analysis and can even be subtle, especially in a NS context. In this study, we used time-varying statistical distributions with parameters conditional on covariates representing the time and CIs. The daily maximum temperatures (DMTs) observed at the Orange and Dijon stations in France were used as a case study. This paper highlights a possible relationship between some large-scale climate patterns and the heat spells in France. Overall, the results suggest that considering the combined effect of global warming and these patterns in NS models is useful for a more appropriate characterization of the hazard heat spells in France.

Abstract Surface Temperature (ST) over India has increased by ~0.055 K/decade during 1860–2005 and follows the global warming trend. Here, the natural and external forcings (e.g., natural and anthropogenic) responsible for ST variability are studied from Coupled Model Inter-comparison phase 5 (CMIP5) models during the 20th century and projections during the 21st century along with seasonal variability. Greenhouse Gases (GHG) and Land Use (LU) are the major factors that gave rise to warming during the 20th century. Anthropogenic Aerosols (AA) have slowed down the warming rate. The CMIP5 projection over India shows a sharp increase in ST under Representative Concentration Pathways (RCP) 8.5 where it reaches a maximum of 5 K by the end of the 21st century. Under RCP2.6 emission scenarios, ST increases up to the year 2050 and decreases afterwards. The seasonal variability of ST during the 21st century shows significant increase during summer. Analysis of rare heat and cold events for 2080–2099 relative to a base period of 1986–2006 under RCP8.5 scenarios reveals that both are likely to increase substantially. However, by controlling the regional AA and LU change in India, a reduction in further warming over India region might be achieved.

In this study, we analysed the tree-ring metal concentrations and isotope ratios of five stands located in three contrasted settings to infer the diffuse air pollution history of the northern part of the Windsor–Québec City Corridor in eastern Canada. Tree-ring series show that the Cd and Zn accumulation rates were higher between 1960 and 1986 and that the long-term acidification of the soil (Ca/Al series) was likely induced by NOx and SOx deposition (δ15N and δ13C trends as proxy). The Pb concentrations and 206Pb/207Pb ratios indicate that the dominant source of lead from 1880 to the 1920s was the combustion of north-eastern American coal, which was succeeded by the combustion of leaded gasoline from the 1920s to the end of the 1980s. Our modelling approach allows separating the climatic and anthropogenic effects on the tree-ring δ13C and δ18O responses. Diffuse air pollution caused an enrichment in 13C in all stands and a decrease of the δ18O values only in three of the stands. This study indicates that dendrogeochemistry can show contrasted responses to environmental changes and that the combination of several independent indicators constitutes a powerful tool to reconstruct the air pollution history in the complex context of peri-urban regions.

In recent years in North America, a number of government agencies and industries have begun to reinvest in meteorological networks. This investment must be based on sound scientific advice. Increased meteorological station network density can be beneficial for a number of purposes, including flood forecasting. This study aimed at investigating the impact of network density at two temporal scales, i.e. for the estimation of total annual precipitation and for the estimation of daily precipitation during specific rain events. This was done using kriging as a means to estimate the spatial distribution and variance of rainfall. Kriged precipitation from two network scenarios (sparse and dense) were used as input into the HSAMI hydrological model and simulations were compared on five drainage basins in the Mauricie area (Québec, Canada). A comparison of the distribution of total annual precipitation interpolated from the two network scenarios showed that adding stations changed the distribution and magnitude of rainfall in the study area. High precipitation cells were better defined with the denser network, and decreases in the relative spatial variance were observed. Similarly, kriged daily precipitation provided a more defined spatial distribution of rainfall during important rain events of 1999, and variance was also reduced when the denser network was used. Finally, simulations performed with the HSAMI model were generally improved when the precipitation inputs were estimated using a denser station network for most drainage basins studied, as expressed by increased Nash coefficients and a decreased root-mean-square error. Peak flows during important summer flood events were generally better simulated when a denser network was used to calculate the mean daily precipitation used as input. Total cumulated volume estimations during the rain events were also generally improved with a denser network. This study showed that the estimation of variance remains an important tool for rain gauge network design. Moreover, network density was shown to have an important impact on the quality of flow simulations, even when a lumped model is used. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Most often, flood frequency analysis describes a flood event only by its peak. However, the true flood severity is also defined by its volume and duration. This paper presents an approach allowing flood events to be considered in a more complete way: the flood-duration–frequency (QdF) approach. In a similar manner to the rainfall intensity–duration–frequency analysis, averaged discharges are computed over different fixed durations d. For each duration a frequency distribution of maximum averaged discharges is studied. Finally, a continuous formulation is fitted, as a function of the return period T and the duration d over which discharges have been averaged. The proposed model has been tested for 169 catchments in the provinces of Quebec and Ontario, Canada. The shapes of the QdF curves enabled us to define different types of flood behaviour and to identify the corresponding geographic regions. This mapping of flood behaviour was the basis for the delineation of seven homogeneous geographical regions, containing catchments having the same hydrological behaviour as is required for regional flood frequency analysis. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R(2). This study found a model that explains 68% (p