Translating hydrodynamic flood impacts into actionable emergency protocols under deep uncertaintyTian, Yuan (Avtor) Bezak, Nejc (Mentor) Johnen, Gregor (Komentor) Huber, Sabrina (Komentor) master thesistransboundary flood risk managementdynamic adaptive policy pathways (DAPP)decision making under deep uncertainty (DMDU)patient rule induction method (PRIM)hydrodynamic exposure assessmentemergency evacuation protocolsClimate change is intensifying extreme flood events, challenging the foundational operation of flood risk management in transboundary river basins. This thesis evaluates and optimizes transboundary flood evacuation protocols under deep uncertainty for the Dinkel River basin, a German-Dutch catchment characterized by rapid flood propagation and institutional fragmentation. Integrating high-resolution 2D hydrodynamic modeling, Latin Hypercube Sampling, and the Patient Rule Induction Method (PRIM), this study identifies critical Adaptation Tipping Points (ATPs) within the current emergency response system. The analysis reveals that under extreme 200 mm extreme precipitation scenario, the evacuation system fails when cross-border coordination delays exceed 1.1 hours and civilian evacuation compliance falls below 33%. Furthermore, while non-structural measures, such as enhanced early warning systems and fleet expansion, provide initial resilience, they exhibit a hard effectiveness ceiling. On the other hand, structural interventions, including urban levees and upstream afforestation, extend coping capacity but inevitably reach physical limits under extreme forcing. Synthesizing these findings within a Dynamic Adaptive Policy Pathways (DAPP) framework, this research provides a phased roadmap for operational and structural interventions, distinguishing between incremental adaptation and transformational spatial reconfiguration. The methodological framework bridges the gap between quantitative flood modelling and adaptive decision-making, offering a transferable template for transboundary flood emergency planning under deep uncertainty.[Y. Tian]20262026-09-01 08:49:07Magistrsko delo/naloga186406UDK: 556.166:556.536(043.2)VisID: 181515COBISS_ID: 289941251sl