An increasing amount of high-resolution global spatial data are available, and used for various assessments. However, key economic and human development indicators are still mainly provided only at national level, and downscaled by users for gridded spatial analyses. Instead, it would be beneficial to adopt data for sub-national administrative units where available, supplemented by national data where necessary. To this end, we present gap-filled multiannual datasets in gridded form for Gross Domestic Product (GDP) and Human Development Index (HDI). To provide a consistent product over time and space, the sub-national data were only used indirectly, scaling the reported national value and thus, remaining representative of the official statistics. This resulted in annual gridded datasets for GDP per capita (PPP), total GDP (PPP), and HDI, for the whole world at 5 arc-min resolution for the 25-year period of 1990–2015. Additionally, total GDP (PPP) is provided with 30 arc-sec resolution for three time steps (1990, 2000, 2015).

The distance between the origin and endpoint of food supply, and the 'localness' of food systems, are key considerations of many narratives associated with sustainability. Yet, information on the minimum distance to food crops is still scarce at the global level. Using an optimisation model based on 'foodsheds' (i.e. self-sufficient areas with internal dependencies), we calculate the potential minimum distance between food production and consumption for six crop types around the world. We show that only 11-28% of the global population can fulfil their demand for specific crops within a 100 km-radius, with substantial variation between different regions and crops. For 26-64% of the population, that distance is greater than 1000 km. Even if transnational foodsheds were in place, large parts of the globe would still depend on trade to feed themselves. While yield gap closure and food loss reductions could favour more local food systems, particularly in Africa and Asia, global supply chains would still be needed to ensure adequate and stable food supply.

Abstract. The Surface Urban Energy and Water Balance Scheme (SUEWS) is developed to include snow. The processes addressed include accumulation of snow on the different urban surface types: snow albedo and density aging, snow melting and re-freezing of meltwater. Individual model parameters are assessed and independently evaluated using long-term observations in the two cold climate cities of Helsinki and Montreal. Eddy covariance sensible and latent heat fluxes and snow depth observations are available for two sites in Montreal and one in Helsinki. Surface runoff from two catchments (24 and 45 ha) in Helsinki and snow properties (albedo and density) from two sites in Montreal are also analysed. As multiple observation sites with different land-cover characteristics are available in both cities, model development is conducted independent of evaluation.The developed model simulates snowmelt related runoff well (within 19 % and 3 % for the two catchments in Helsinki when there is snow on the ground), with the springtime peak estimated correctly. However, the observed runoff peaks tend to be smoother than the simulated ones, likely due to the water holding capacity of the catchments and the missing time lag between the catchment and the observation point in the model. For all three sites the model simulates the timing of the snow accumulation and melt events well, but underestimates the total snow depth by 18-20 % in Helsinki and 29-33 % in Montreal. The model is able to reproduce the diurnal pattern of net radiation and turbulent fluxes of sensible and latent heat during cold snow, melting snow and snowfree periods. The largest model uncertainties are related to the timing of the melting period and the parameterization of the snowmelt. The results show that the enhanced model can simulate correctly the exchange of energy and water in cold climate cities at sites with varying surface cover.

Abstract. The Surface Urban Energy and Water balance Scheme (SUEWS) is developed to include snow. The processes addressed include accumulation of snow on the different urban surface types; snow albedo and density aging; snow melting and re-freezing of melt water. Individual model parameters are assessed and independently evaluated using long-term observations in two cold climate cities, Helsinki and Montreal. Eddy covariance sensible and latent heat fluxes and snow depth observations are available for two sites in Montreal and one in Helsinki. Surface runoff from two catchments (24 and 45 ha) in Helsinki and snow properties (albedo and density) from two sites in Montreal are also analysed. As multiple observation sites with different land-cover characteristics are available in both cities, model development is conducted independently of evaluation. The developed model simulates snowmelt related runoff well (within 10% and 6% for the two catchments in Helsinki when there is snow on the ground), with the springtime peak estimated correctly. However, the observed runoff peaks tend to be smoother than the simulated ones, likely due to the water holding capacity of the catchments and the missing time lag between the catchment and the observation point in the model. At all three sites the model simulates snow accumulation and melt events well, but underestimates snow depth by 18–20% in Helsinki and 29–33% in Montreal. The model is able to reproduce the diurnal pattern of net radiation and turbulent fluxes of sensible and latent heat during cold snow, melting snow and snow free periods. Largest model uncertainties are related to the melting period. The results show that the enhanced model can correctly simulate the exchange of energy and water in cold climate cities, and is appropriate to be nested in a larger scale atmospheric model or used independently for urban planning.