Modeling, Foresight and Risk Assessment Capacity

Sustainability science needs relevant data, strong modeling and foresight capacities. Cutting across thematic areas are methodological innovations in modeling at different scales and across them (upscaling and downscaling) with use of new large-scale data.

The research areas considered in this context include:

  • Monitoring and modeling the earth, its change and changing use, both terrestrial and aquatic. Central aspects are global change and innovative climate modeling science for sustainable development, incl. carbon and water cycles, but also modeling land-use change and biodiversity. Important research questions focus on the irreversible effects of human activity on the geosphere („Anthropocene“) including geosciences, area studies, development studies, and also economic research. Integrated monitoring systems aim for comprehensive information over continental areas on the usability/availability of renewable energy (wind, solar radiation, geothermal), regional climate, water resources and nutrients (groundwater, surface water, soil health) for food and water management, impacted by global change. This includes hazards, risks and uncertainties, and potentially makes use of current and next-generation high-performance computing systems. This also includes the understanding and modeling indirect and direct health effects of economic, industrial and agricultural development for the development of sustainable solutions that consider synergetic effects, tradeoffs and mitigation of health risks.

  • Modeling actors’ options together with bio-physical modeling: current and future use of natural resources, agricultural production, and processing technologies for bio-based materials that capture choices of actors on their course of action under economic objectives and conditions are combined with bio-physical modeling. Such model design allows assessing the impact of regulatory interventions to the system with endogenous actor behavior. It includes dynamic modeling in order to better capture uncertainty, volatility, and emerging properties of social agricultural systems in assessing management and regulatory options, using numerical algorithms to model technology adoption behavior, endogenous institutions and emergent properties from dynamic agent interaction. Different international collaborative research for example on innovative crop modeling adds insights here, and shall be linked with socio-economic models to research global change impacts on agriculture and of adaptation options to improve food security, climate resilience and sustainability.

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