Snowmelt is a critical component of the global water cycle and a vital freshwater source for both ecosystems and human societies. Yet the global partitioning of snowmelt into runoff and evaporation remains poorly quantified. Here, using a process-based hydrological model (FLEX-Global) forced by meteorological data from 1980 to 2014 and validated against observed streamflow and snow water equivalent, we present a comprehensive assessment of global snowmelt partitioning. The model results are independently supported by two additional approaches: an empirical partitioning equation and inverse estimations from three global hydrological models. We show that 53%–71% of snowmelt runs off globally (excluding Antarctica and Greenland), while 29%–47% contributes to evaporation. Snowmelt partitioning exhibits distinct latitudinal and climatic patterns: contributions of snowmelt to both runoff and evaporation increase with latitude. In cold–humid high-latitude regions, more than 60% of snowmelt becomes runoff, whereas in mid-latitude arid regions, 63%–91% is released from the terrestrial ecosystems as evaporation. Elevation further modulates snow hydrology in mid-latitude mountains, where snowmelt generates 58%–74% of total runoff and 51%–66% of total evaporation—significantly higher than contributions at lower elevations. The traditional definition of snowmelt runoff (snowfall/total runoff) estimates that snowfall accounts for 38% of total runoff, whereas our snowmelt-partitioning approach (snowmelt runoff/total runoff) yields a much lower contribution of 11%–18%. Our results underscore snowmelt's dual role in sustaining freshwater availability and supporting vegetation water demand, redefining its importance in the global hydrological cycle and associated ecosystem services.

While mobility and safety of drivers are challenged by behavioral changes, the increasingly complex road environment has placed a higher demand on their adaptability. The ultimate goal of this paper was to identify the impact that the balance between task complexity and coping capacity had on crash risk. Towards that aim, an integrated model for understanding the effect of the inter-relationship of task complexity and coping capacity with risk was developed. A vast library of data from a naturalistic driving experiment was created in three countries (i.e., Belgium, UK and Germany) to investigate the most prominent driving behavior indicators available, including speeding, headway, overtaking, duration, distance and harsh events. In order to fulfil the aforementioned objectives, exploratory analysis, such as Generalized Linear Models (GLMs) were developed, and the most appropriate variables associated to the latent variable “task complexity” and “coping capacity” were estimated from the various indicators. Additionally, Structural Equation Models (SEMs) were used to explore how the model variables were inter-related, allowing for both direct and indirect relationships to be modelled. The analyses revealed that higher task complexity levels lead to higher coping capacity by drivers. Additionally, the effect of task complexity on risk was greater than the impact of coping capacity in Belgium and Germany, while mixed results were observed in the UK.