The United States Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) alert system provides rapid (10–20 min) but general loss estimates of ranges of fatalities and economic impact for significant global earthquakes. FEMA’s Hazus software, in contrast, provides time-consuming (2–5 h) but more detailed loss information quantified in terms of structural, social, and economic consequences estimated at a much higher spatial resolution for large domestic earthquakes. We developed a rapid hybrid post-earthquake product that takes advantage of the best of both loss models. First, though, we conducted a systematic comparison of loss estimates from PAGER with Hazus for all significant, relatively recent, domestic earthquakes for which adequate loss data exist—augmented by a dozen ShakeMap scenarios. The systematic comparison of Hazus and PAGER losses provided the basis for selecting the specific loss metrics to present from each system. The signature product will serve as a supplement to the widely deployed PAGER alert product for significant domestic earthquakes.

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Flood risk planning and emergency response at community levels rely on fast access to accurate inundation models that identify geographic areas, assets, and populations that may be flooded. However, limited flood modelling resources are available to support these events and activities. We present a computationally-efficient flood model for facilitating rapid risk analysis across a wide range of scenarios and decision support to operational, crisis action, local flood-fight, and community planning efforts. Our flood depth regression method converts publicly-available river stage heights to flood depths, then downscales the depths from gage locations onto high resolution National Hydrography Dataset flowlines and estimates areas and depths of flooding by subtraction of the National Elevation Dataset from modelled water surface elevations. We demonstrate proof-of-principle analyses for historic 2009 Red River of the North flooding in the United States, achieving comprehensive mainstem flood estimation for the length of the river and depth accuracy of 1.4 ft (0.4 m) compared to gage observations, remote sensing, and higher-resolution hydrologic models. We