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Significance of storm spatiotemporal variability and movement in flood hydrodynamic modelling
(2024)
This dissertation aims to explore the impact of various storm properties on computational flood modelling and to propose a method for integrating them into flood risk assessment. It focuses on three key areas:
Firstly, it examines within-storm variability, investigating how variations in storm intensity, duration, and spatial distribution affect runoff dynamics. Through a systematic theoretical approach, the study generates synthetic rainfall signals with different hyetograph variabilities and applies them to a hydrodynamic model. The results highlight the significant influence of storm spatiotemporal variability on runoff response, affecting peak discharge, hydrograph shapes, and flooded areas. Temporal variability emerges as particularly dominant, overshadowing spatial variability and other storm properties such as return period and volume. The study emphasizes the spatial dependency of these effects within the drainage network.
Secondly, it investigates the role of storm movement in flood modelling, recognizing its potential to alter runoff dynamics and inundation patterns. By generating synthetic rain hyetographs traversing the catchment area at varying velocities and directions, the study shows that storm movement significantly impacts runoff response and flood extents. Higher storm velocities lead to more pronounced peaks and faster runoff onsets, resulting in larger flooded areas. The direction of storm movement plays a crucial role, with storms aligning with the average stream direction causing the highest peaks and flooded areas. The magnitude of this influence varies depending on the location within the catchment.
Finally, the dissertation introduces a dynamic spatiotemporal rainfall model aimed at preserving storm event properties. Using an event-based approach, dynamic precipitation events are identified and regenerated, ensuring the conservation of storm spatiotemporal variability and movement characteristics. This facilitates the generation of more physically plausible and spatiotemporally coherent precipitation time series. The model prioritizes user accessibility, offering a practical tool for integrating storm properties into flood computation modelling and risk analysis frameworks. By addressing these aspects of storm behaviour, the dissertation contributes to enhancing the accuracy and effectiveness of flood modelling, thereby improving flood risk mitigation efforts.