Modelling and projecting urban land cover

As previous chapters in this book have shown, urban expansion is an ongoing process with considerable impacts on the environment, the economy and quality of life. Europe, with its largely urban population, is no exception. To curtail the negative impacts and foster the positive effects of ongoing urban expansion, policies will have to be adjusted and harmonised. To do so an outlook of future land use and urbanisation trends is indispensable. Such an analysis of evolutions and functional profiles of European cities requires evaluating the impacts of continent-wide drivers and, at the same time, the effect of national and local strategies with their own priorities and plans

Assessing uncertainties in land cover projections.

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover