Berlin Metropolitan Area
Coordinating state of the art global land-use modelling research from a Research Software Engineering perspective and improving representations of cross-scale dynamics.
Software has become an integral part of research. The path to scientific knowledge today leads almost exclusively through the use of software, which is used for data acquisition, analysis and modeling. This applies in particular to research at PIK with its strong focus on modeling. Software therefore plays a central role in ensuring our high scientific quality. The demands placed on research in terms of transparency, reproducibility and robustness also apply to the correlated software. We as Research Software Engineers focus on meeting these criteria: Better software enables better research!
Thesis: Efficient treatment of cross-scale interactions in a land-use model Abstract: Computer models have become a common tool in various disciplines. A major challenge in modeling is the linking of processes on different scales. Neglecting cross-scale interactions leads to biases in model projections while a 1:1 representation is computational infeasible. Therefore, a good balance between accuracy and abstraction is essential. I investigate efficient implementations of cross-scale interactions in agricultural land-use models. I focus on two dominant aspects: First, the inclusion of spatially explicit data in a global optimization model; second, the proper representation of technological change as a driver for land use change. As a consequence of limitations in complexity of global optimization models the problem arises that high-resolution data cannot be used directly as model input. Typically, the spatially explicit data is upscaled by using a static upscaling rule. As an alternative I discuss the use of clustering methods for upscaling. I provide a general framework including the creation of clusters, the upscaling of inputs, and the downscaling of outputs. My investigations show that the information loss due to upscaling decreases significantly with cluster methods compared to static grids. Another important process in agriculture is technological change. Whereas in the past increases in agricultural production were mainly achieved by agricultural land expansion, nowadays most increases in total production are outcome of intensification due to technological change. To model this feedback I introduce a measure for agricultural land-use intensity. Based on this measure I show that the effectiveness of investments in technological change decreases with the agricultural land-use intensity.