BERLIN
Responsible:
Collaboration with Academic Institutions and Research Centres
DEIB Role: Coordinator
Start date: 2025-11-01
Length: 36 months
Project abstract
In a changing climate and society, water storage systems can be critical for the achievement of several Sustainable Development Goals (SDGs). Today, more than 58,000 large dams control 46% of the world’s largest rivers. The dynamics of these rivers are determined not only by their natural hydrologic regime but also by the influence of human agents, who decide how much water to store in the reservoir impounded by the dams and how much to release to serve the demands of different stakeholders.
In this context, BERLIN will make substantial progress in understanding and modeling how the behavior of human agents influences the complex dynamics of coupled human-natural systems. To achieve this objective, BERLIN will construct data-driven behavioral models of the agents’ intentions and preferences by leveraging the recent advances in Machine Learning, which allow exploiting the full potential of the unprecedented availability of big observational data. Moreover, BERLIN will address risk assessment by delving into stakeholders’ experiences and preferences from a triple-loop approach (risk awareness, risk perception, and risk adaptation) to explore how Social Learning and user-driven indicators can reinforce the model-based exploration of adaptation policies. Climate narratives and storylines will be constructed to represent self-consistent past events and the plausibility of different adaptation pathways.
The integration of these transdisciplinary research efforts will support the development of a behaviorally explicit global hydrologic model for supporting rigorous retrospective assessments of the observed agents’ behaviors as well as the development of reliable and credible projections about the future coevolution of coupled human-natural systems. To provide an evidence-based foundation for the identification of adaptation policies at the local scale, BERLIN will consider different Climate Change Hotspots including semiarid regions, river deltas, and snow- dependent river basins.
As a result, BERLIN will open a new path for modeling human behaviors, supporting the codesign of local adaptation strategies as well as the achievement of regional-to-global scale targets related to water management, energy and food security as part of the SDGs.
In this context, BERLIN will make substantial progress in understanding and modeling how the behavior of human agents influences the complex dynamics of coupled human-natural systems. To achieve this objective, BERLIN will construct data-driven behavioral models of the agents’ intentions and preferences by leveraging the recent advances in Machine Learning, which allow exploiting the full potential of the unprecedented availability of big observational data. Moreover, BERLIN will address risk assessment by delving into stakeholders’ experiences and preferences from a triple-loop approach (risk awareness, risk perception, and risk adaptation) to explore how Social Learning and user-driven indicators can reinforce the model-based exploration of adaptation policies. Climate narratives and storylines will be constructed to represent self-consistent past events and the plausibility of different adaptation pathways.
The integration of these transdisciplinary research efforts will support the development of a behaviorally explicit global hydrologic model for supporting rigorous retrospective assessments of the observed agents’ behaviors as well as the development of reliable and credible projections about the future coevolution of coupled human-natural systems. To provide an evidence-based foundation for the identification of adaptation policies at the local scale, BERLIN will consider different Climate Change Hotspots including semiarid regions, river deltas, and snow- dependent river basins.
As a result, BERLIN will open a new path for modeling human behaviors, supporting the codesign of local adaptation strategies as well as the achievement of regional-to-global scale targets related to water management, energy and food security as part of the SDGs.
