{"id":4337,"date":"2023-02-16T15:22:37","date_gmt":"2023-02-16T15:22:37","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/meetings2-2\/?page_id=4337"},"modified":"2026-04-22T12:50:48","modified_gmt":"2026-04-22T12:50:48","slug":"keynotes","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/wsc2026\/keynotes\/","title":{"rendered":"Keynotes"},"content":{"rendered":"<\/p>\n\n
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\n \n 2026 WSC Opening Keynote <\/span>\n\n \n \n <\/span>\n <\/h2>\n<\/div>\n\n
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\n \"Ralf \n <\/div>\n \n \n
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\n Ralf Ludwig <\/h3>\n
\n Professor for Applied Physical Geography and Environmental Monitoring, Dean of Studies at the Faculty of Geosciences
\nLMU Munich
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Why Scenarios? Using Simulations for Perspectives on Hydroclimatic Futures between Utopia and Dystopia<\/strong><\/p>\n

Climate scenarios are structured representations of possible future climate conditions based on assumptions about greenhouse gas emissions, socio-economic development, and technological pathways. They form the backbone of modern climate resilience research, enabling systematic exploration of how hydroclimatic extremes \u2014 including floods, droughts, heatwaves, and compound events \u2014 may evolve under alternative futures. Such scenario-based simulations are indispensable for anticipating risks, supporting adaptive planning, and guiding sustainable water resources management in a non-stationary climate.<\/p>\n

Yet, generating robust and decision-relevant hydroclimatic projections remains a grand computational challenge. High-resolution, process-based Earth system, regional climate, and hydrological models must be coupled across scales, often through large ensemble frameworks to sample internal variability and scenario uncertainty. This leads to massive computational demand, requiring High Performance Computing (HPC) infrastructures capable of handling petabyte-scale data streams, sub-kilometer-scale grid resolutions, and long transient integrations. HPC thus becomes not only a technical necessity, but a scientific enabler, allowing the representation of extremes, feedbacks, and cross-system interactions that are critical for resilience assessments.<\/p>\n

Beyond traditional simulation paradigms, the presentation explores the emerging role of physics-aware AI. By embedding physical constraints into machine learning architectures, these hybrid approaches accelerate surrogate modeling, improve downscaling, detect compound risk patterns, and help identify non-linear regime shifts that may remain hidden in classical analyses. Rather than replacing process understanding, physics-aware AI augments it \u2014 opening new avenues for scenario discovery, uncertainty quantification, and rapid exploration of \u201cwhat-if\u201d futures.<\/p>\n

The talk introduces the conceptual foundations of hydroclimatic scenarios and demonstrates their relevance for integrated water resources management through contrasting best-case and worst-case examples from North America and Europe. Framing futures between utopia and dystopia, it argues that advanced simulation ecosystems \u2014 combining HPC and physics-informed AI \u2014 are central to turning climate scenarios into actionable resilience knowledge.<\/p> <\/div>\n<\/div>\n\n

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