A06
Processes and determinants of climate-relevant landscape configurations
Prof. Dr. Jan Börner
University of Bonn | +49-228-733548 |
Prof. Dr. Thomas Heckelei
University of Bonn | +49-228-732332 |
Prof. Dr. Silke Hüttel
University of Göttingen | +49 551 39-24851 |
Summary
In this project, we explore land use change and land cover change (LULCC) and seek to understand spatiotemporal landscape dynamics in the study area of the CRC. Our focus lies on identifying historical and contemporary determinants relevant for the composition and configuration of landscape elements that regulate coupled land and atmospheric water and energy cycles. This includes crops, forests/trees, and grasslands. We use modelling and econometric techniques to quantify potential LULCC determinants, such as economic trends, agricultural market dynamics, infrastructure investments and related risks, and policies at various administrative scales.
Graphical summary
Contribution to the CRC
The knowledge generated in this project and in the partner project A05 will inform LULCC scenario development for earth system modelling in the CRC. The CRC’s central hypothesis is that LULCC matters for regional climate dynamics. This project quantifies what matters for LULCC and thus enables us to identify entry points for climate smart land use governance.
Approach
Our work to explore these hypotheses is structured in three work packages. First, we develop a conceptual framework linking climate-relevant landscape characteristics (e.g., connectivity, albedo, land use intensity) with determinants of landscape dynamics. The framework development departs from decision-theoretical considerations and is complemented by theories of land system change. Second, we construct a consistent spatially explicit dataset of LULCC metrics and predictor variables informed by our conceptual framework. Here we closely collaborate with natural scientists and earth system modelling teams of the CRC to construct LULCC outcome metrics that capture climate-relevant landscape characteristics. Third, we test our hypotheses using spatial multivariate and econometric approaches including estimation techniques based on machine learning.
Main results in 2022
Development of a conceptual framework to identify climate relevant landscape compositions and configurations.
Main results in 2023
Planned: Selection of LULCC metrics for econometric analysis and data base development.
