Humans actively shape the terrestrial biosphere in order to produce essential resources such as food, fiber, and wood as well as for settlements, industries, and infrastructure. Their activities also affect climate, oceans, and the functioning of the Earth System and, thus, change the terrestrial biosphere also indirectly. It is important to understand the processes, dynamics, and interactions of the Earth System in order to assess the consequences of human activity, such as large-scale fossil fuel combustion or tropical deforestation. With the help of computer models, the future development of the Earth System can be projected into the future under different scenarios of societal development.

This study focuses on the effects of human land use and climate change on the global terrestrial biosphere. I demonstrate the importance of land use and land-use change for the global terrestrial carbon and water cycles in two different analyses: In the first, I generated stylized spatially explicit land-use data to explore the effects of changes in socio-economic drivers. In the second, I used the consistent land-use and climate data sets generated by the IMAGE 2.2 model for the Special Report on Emission Scenarios (SRES) A2, B1, and B2. Both analyses show that the effects of land use and land-use change on the global terrestrial carbon cycle are equally important to the effects of CO2 fertilization and climate change, causing terrestrial carbon losses of up to 450PgC under the A2 scenario. For the terrestrial water cycle, land use mainly results in reduced transpiration and increased evaporation fluxes, with little effects on runoff at the global scale.

The rate of land-use change and the spatial localization of agricultural production are of major importance for the effects of land use on the terrestrial biosphere. However, spatially explicit future projections are hardly available. To overcome this imbalance between importance and availability, a globally applicable, spatially explicit land-use model is needed. I provide an overview of existing large-scale land-use models and approaches. Besides disciplinary approaches, integrated approaches that combine economic and geographic methodologies exist, but suffer from inconsistencies and incomplete linkages. A major obstacle in integrating economic and geographic approaches is the difference in spatial scales. To bridge the gap between these spatial scales, I explore the robustness of Dynamic Global Vegetation Model (DGVM) simulations against reductions in spatial resolution. Coarser spatial resolutions do not differ qualitatively from finer spatial grids, as the deviation from the typically used 0.5 degree grid increases linearly with grid coarseness with a small slope (less than 1.5 percent deviation per degree).

As an outlook, I introduce a newly developed globally applicable land-use model, MAgPIE, an economic optimization model, which generates spatially explicit land-use patterns. Essential inputs are spatially explicit data on yield levels and freshwater availability and regional data on population, production costs, and Gross Domestic Product (GDP) only. MAgPIE internally computes changes in diets, and thus demand, based on empirical relations to GDP if no suitable input data are available. Besides generating spatially explicit land-use patterns, MAgPIE allows for exploring the effects of technology change and trade liberalization, and for valuating the competition for land and water.

More information at http://www.pik-potsdam.de/members/cmueller