Typically a series of flood maps with a defined probability of occurrence Produce synthetic realisations of flooding. Instead, these limited observations are used to drive physical models to Their flood behaviour to adequately characterise the hazard they pose alone. Few stretches of river contain enough observations of The severity of riverine flood hazards is principally understood through This analysis highlights the continually improving fidelity of large-scale flood hazard models, yet also evidences the need for considerable advances in the accuracy of routinely collected field and high-river flow data in order to interrogate flood inundation models more comprehensively. Despite this, the model generally replicated the observed flood events in the context of terrain data vertical accuracy, extreme discharge measurement uncertainties, and observational field data errors. While all events were accurately replicated in terms of flood extent, some modelled water levels deviated substantially from those measured in the field. Model performance was highly variable, depending on the flood event chosen and validation data used. A total of 35 discrete events are modelled and compared to observations of flood extent, water level, and inundated buildings. Here, we adapt an existing continental-scale design flood framework of the contiguous US to simulate historical flood events. However, both types of model may share the same biases and so not validly Since they output phenomena unobservable in reality, large-scale models are typically compared to more localised engineering models to evidence their accuracy. theoretical flood events of a given probability. Continental–global-scale flood hazard models simulate design floods, i.e.
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