Intensity–duration–frequency (IDF) curves of precipitation are a reference decision support tool used in hydrology. They allow the estimation of extreme precipitation and its return periods. Typically, IDF curves are estimated using univariate frequency analysis of the maximum annual intensities of precipitation for different durations. It is then assumed that the annual maxima of different durations are independent to simplify the parameter estimation. This strong hypothesis is not always verified for every climatic region. This study examines the effects of the independence hypothesis by proposing a multivariate model that considers the dependencies between precipitation intensities of different durations. The multivariate model uses D-vine copulas to explore the intraduration dependencies. The generalized extreme values distribution (GEV) is considered a marginal model that fits a wide range of tail behaviors. An illustration of the proposed approach is made for historical data from Moncton, in the province of New Brunswick (Eastern Canada), with climatic projections made through three scenarios of the Representative Concentration Pathway (RCP).

This paper features an application of Regular Vine (R-vine) copulas, a recently developed statistical tool to assess composite risk. Copula-based dependence modelling is a popular tool in conditional risk assessment, but is usually applied to pairs of variables. By contrast, Vine copulas provide greater flexibility and permit the modelling of complex dependency patterns using a wide variety of bivariate copulas which may be arranged and analysed in a tree structure to explore multiple dependencies. This study emphasises the use of R-vine copulas in an analysis of the co-dependencies of five reservoirs in the cascade of the Saint-John River basin in Eastern Canada. The developed R-vine copulas lead to the joint and conditional return periods of maximum volumes, for hydrologic design and cascade reservoir management in the basin. The main attraction of this approach to risk modelling is the flexibility in the choice of distributions used to model heavy-tailed marginals and co-dependencies.

This study analyses the combined impact of climate change and population dynamics on energy demand in New Brunswick, Canada. We identify the regions where changes in energy demand are expected to be most pronounced and examine how the spatial concentration of populations in urban cores will interact with local climatic trends. To this end, we develop a spatially explicit framework that integrates climate data from CMIP6 simulations with socioeconomic projections from the shared socioeconomic pathways (SSPs). This framework enables the estimation of population-weighted heating and cooling degree days (CDDs) for both current and future population distributions. We evaluate three combined climate–socioeconomic scenarios: SSP1-2.6 (optimistic), SSP2-4.5 (moderate), and SSP5-8.5 (pessimistic). While the results confirm a substantial reduction in heating demand (up to − 50% under SSP5-8.5) and a sharp increase in cooling demand (up to +1000%), the granular analysis reveals pronounced spatial heterogeneity. Cooling demand is projected to become highly concentrated along the province’s central axis, with Moncton, Fredericton, and Saint John emerging as major hotspots, whereas rural communities are expected to experience a more rapid decline in heating needs. Although overall provincial population growth has a limited effect, projected urbanization significantly intensifies these trends, resulting in an average 5% decrease in heating demand and an average 8% increase in cooling demand across all scenarios. Under SSP5-8.5, urbanization leads to an additional 18.3 CDDs and 210 fewer heating degree days, thereby amplifying the urban heat island effect in major urban centres.