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Global flood models integrate flood maps of constant probability in space, ignoring the correlation between sites and thus potentially misestimating the risk posed by extreme events. Stochastic flood models alleviate this issue through the simulation of flood events with a realistic spatial structure, yet their proliferation at large scales has historically been inhibited by data quality and computer availability. In this paper, we show, for the first time, the efficacy of modeled river discharge reanalyses in the characterization of flood spatial dependence in the absence of a dense stream gauge network. While global hydrological models may show poor correspondence with absolute observed river flows, we find that the rate at which they can simulate the joint occurrence of relative flow exceedances at two given locations is broadly similar to when a gauge‐based statistical model is used. Evidenced over the United States, flood events simulated using observed gauge data from the U.S. Geological Survey versus those generated using modeled streamflows have similar (i) distributions of site‐to‐site correlation strength, (ii) relationships between event size and return period, and, importantly, (iii) loss distributions when incorporated into a continental‐scale flood risk model. Extremal dependence is generally quantified less accurately on larger rivers, in arid climates, in mountainous terrain, and for the rarest high‐magnitude events. However, local‐scale errors are shown to broadly cancel each other out when combined, producing an unbiased flood spatial dependence model. These findings suggest that building accurate stochastic flood models worldwide may no longer be a distant aspiration. Plain Language Summary: Global flood risk is commonly estimated through flood inundation maps with a defined probability of occurrence. These flood simulations have a key drawback in that they fail to capture the spatial patterns exhibited during real flood events, instead modeling the same probability of flooding on every river at once. Solutions which rely on networks of gauged river flow observations will necessarily break down in the majority of the world's regions which lack such a resource. In this paper, we use historic river flows simulated by global rainfall‐runoff models (rather than observed flows) into a statistical model which captures the spatial correlation of flow extremes. If we examine the relative flow exceedance probabilities from these hydrological models rather than the volumetric flow values, flood events are generated which exhibit similar characteristics to those when gauged flow observations are used. Crucially, the simulation‐ and observation‐generated flood events produce near‐identical losses to buildings in the United States. The implications of this are that true stochastic flood risk models, which account for spatial dependence, can proliferate globally via the generation of realistic flood event sets from hydrological models. Key Points: Large‐scale flood hazard models typically neglect to represent the spatial dependence of real flood eventsRelative flow exceedances simulated by the fusion of global hydrological with statistical models reproduce gauge‐driven flood event setsAt the continental scale, key characteristics of a flood risk model are indistinguishable when driven with observed versus modeled flow data [ABSTRACT FROM AUTHOR]

Continental scale flood inundation models have undergone rapid development in the past five years. In data rich locations, such as the conterminous United States, accurate DEM data and high-performance computing resources allow floodplain flows to be resolved in great detail at high resolution (30 m or less). However, all fluvial flooding is governed by the conveyance capacity of the river network and thus accurately representing the expected conveyance within the inundation model is critical for simulation accuracy.In continental scale models, the geometry of the river channel is typically estimated from an expected conveyance capacity using Manning's equation or hydraulic geometry, due to the difficulty of obtaining complete channel bathymetry data over wide areas. However, as these methods all fail to account for backwater effects during simulation the models are susceptible to over prediction of water level, especially during small floods or flows close to bank full discharge. As a result, risk estimates from low return period events can be dramatically overestimated in relation to flood loss data.We present results from a version of the Fathom US flood model where a 1D steady state solver and optimisation technique has been employed to estimate an appropriate channel bed bathymetry for bank full conveyance, allowing for more control over inundation inception. Employing our method reduces losses due to small flood flows and greatly improves the representation of flood defence standards where known. Although defence standards still need to be estimated, our method allows for an accurate representation of the assumed standard within the continental scale model, which was not the case with earlier model structures. [ABSTRACT FROM AUTHOR]