the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Invited perspectives: Fostering interoperability of data, models, communication, and governance for disaster resilience through transdisciplinary knowledge co-production
Pia-Johanna Schweizer
Benedikt Gräler
Lydia Cumiskey
Sukaina Bharwani
Janne Parviainen
Chahan M. Kropf
Viktor Wattin Håkansson
Martin Drews
Tracy Irvine
Clarissa Dondi
Heiko Apel
Jana Löhrlein
Stefan Hochrainer-Stigler
Stefano Bagli
Levente Huszti
Christopher Genillard
Silvia Unguendoli
Fred Hattermann
Max Steinhausen
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We explored how disasters that occur before recovery from earlier events is complete can complicate recovery, using examples from science and the real world across different societal domains. We then reflect on why recovery under repeated disasters is more complex than simply returning to normal, and how the way we define recovery and the system we study shapes our understanding. These insights can help us better understand, study, and manage recovery from disasters.
Urban pluvial flooding is worsening due to climate change and urbanization, requiring faster forecasts. This study presents RIM2D, a multi-graphics processing unit (GPU) 2D flood model, simulating high-resolution events (2–10 m) across Berlin (891.8 km²) with up to 8 GPUs. Simulations of real and synthetic floods show multi-GPU use is vital for fine-scale, timely forecasts. RIM2D proves operationally viable for urban-scale early warning using modern GPU hardware.